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  • 1.
    Abdsharifi, Mohammad Hossein
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Dhar, Ripan Kumar
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Service Management for P2P Energy Sharing Using Blockchain – Functional Architecture2022Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Blockchain has become the most revolutionary technology in the 21st century. In recent years, one of the concerns of world energy isn't just sustainability yet, in addition, being secure and reliable also. Since information and energy security are the main concern for the present and future services, this thesis is focused on the challenge of how to trade energy securely on the background of using distributed marketplaces that can be applied. The core technology used in this thesis is distributed ledger, specifically blockchain. Since this technology has recently gained much attention because of its functionalities such as transparency, immutability, irreversibility, security, etc, we tried to convey a solution for the implementation of a secure peer-to-peer (P2P) energy trading network over a suitable blockchain platform. Furthermore, blockchain enables traceability of the origin of data which is called data provenience.

    In this work, we applied a secure blockchain technology in peer-to-peer energy sharing or trading system where the prosumer and consumer can trade their energies through a secure channel or network. Furthermore, the service management functionalities such as security, reliability, flexibility, and scalability are achieved through the implementation. \\

    This thesis is focused on the current proposals for p2p energy trading using blockchain and how to select a suitable blockchain technique to implement such a p2p energy trading network. In addition, we provide an implementation of such a secure network under blockchain and proper management functions. The choices of the system models, blockchain technology, and the consensus algorithm are based on literature review, and it carried to an experimental implementation where the feasibility of that system model has been validated through the output results. 

    Fulltekst (pdf)
    Service Management for P2P Energy Sharing Using Blockchain – Functional Architecture
  • 2.
    Abghari, Shahrooz
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Data Mining Approaches for Outlier Detection Analysis2020Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Outlier detection is studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modelling the normal behaviour in order to identify abnormalities. The choice of model is important, i.e., an unsuitable data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and requirements of the domain problem. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive. 

    In this thesis, we study and apply a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We focus on three real-world application domains: maritime surveillance, district heating, and online media and sequence datasets. We show the importance of data preprocessing as well as feature selection in building suitable methods for data modelling. We take advantage of both supervised and unsupervised techniques to create hybrid methods. 

    More specifically, we propose a rule-based anomaly detection system using open data for the maritime surveillance domain. We exploit sequential pattern mining for identifying contextual and collective outliers in online media data. We propose a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. We develop a few higher order mining approaches for identifying manual changes and deviating behaviours in the heating systems at the building level. The proposed approaches are shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviours. We also investigate the reproducibility of the proposed models in similar application domains.

    Fulltekst (pdf)
    fulltext
  • 3.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    A Higher Order Mining Approach for the Analysis of Real-World Datasets2020Inngår i: Energies, E-ISSN 1996-1073, Vol. 13, nr 21, artikkel-id 5781Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this study, we propose a higher order mining approach that can be used for the analysis of real-world datasets. The approach can be used to monitor and identify the deviating operational behaviour of the studied phenomenon in the absence of prior knowledge about the data. The proposed approach consists of several different data analysis techniques, such as sequential pattern mining, clustering analysis, consensus clustering and the minimum spanning tree (MST). Initially, a clustering analysis is performed on the extracted patterns to model the behavioural modes of the studied phenomenon for a given time interval. The generated clustering models, which correspond to every two consecutive time intervals, can further be assessed to determine changes in the monitored behaviour. In cases in which significant differences are observed, further analysis is performed by integrating the generated models into a consensus clustering and applying an MST to identify deviating behaviours. The validity and potential of the proposed approach is demonstrated on a real-world dataset originating from a network of district heating (DH) substations. The obtained results show that our approach is capable of detecting deviating and sub-optimal behaviours of DH substations.

    Fulltekst (pdf)
    fulltext
  • 4.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Multi-view Clustering Analyses for District Heating Substations2020Inngår i: DATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications2020, / [ed] Hammoudi S.,Quix C.,Bernardino J., SciTePress, 2020, s. 158-168Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this study, we propose a multi-view clustering approach for mining and analysing multi-view network datasets. The proposed approach is applied and evaluated on a real-world scenario for monitoring and analysing district heating (DH) network conditions and identifying substations with sub-optimal behaviour. Initially, geographical locations of the substations are used to build an approximate graph representation of the DH network. Two different analyses can further be applied in this context: step-wise and parallel-wise multi-view clustering. The step-wise analysis is meant to sequentially consider and analyse substations with respect to a few different views. At each step, a new clustering solution is built on top of the one generated by the previously considered view, which organizes the substations in a hierarchical structure that can be used for multi-view comparisons. The parallel-wise analysis on the other hand, provides the opportunity to analyse substations with regards to two different views in parallel. Such analysis is aimed to represent and identify the relationships between substations by organizing them in a bipartite graph and analysing the substations’ distribution with respect to each view. The proposed data analysis and visualization approach arms domain experts with means for analysing DH network performance. In addition, it will facilitate the identification of substations with deviating operational behaviour based on comparative analysis with their closely located neighbours.

    Fulltekst (pdf)
    Multi-view Clustering Analyses for District Heating Substations
  • 5.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    District Heating Substation Behaviour Modelling for Annotating the Performance2020Inngår i: Communications in Computer and Information Science / [ed] Cellier, P, Driessens, K, Springer , 2020, Vol. 1168, s. 3-11Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this ongoing study, we propose a higher order data mining approach for modelling district heating (DH) substations’ behaviour and linking operational behaviour representative profiles with different performance indicators. We initially create substation’s operational behaviour models by extracting weekly patterns and clustering them into groups of similar patterns. The built models are further analyzed and integrated into an overall substation model by applying consensus clustering. The different operational behaviour profiles represented by the exemplars of the consensus clustering model are then linked to performance indicators. The labelled behaviour profiles are deployed over the whole heating season to derive diverse insights about the substation’s performance. The results show that the proposed method can be used for modelling, analyzing and understanding the deviating and sub-optimal DH substation’s behaviours. © 2020, Springer Nature Switzerland AG.

  • 6.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Lavesson, Niklas
    Jönköping University, SWE.
    Higher order mining for monitoring district heating substations2019Inngår i: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, s. 382-391Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. © 2019 IEEE.

    Fulltekst (pdf)
    Higher Order Mining for Monitoring DistrictHeating Substations
  • 7.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Exner, Peter
    Sony R&D Center Lund Laboratory, SWE.
    An Inductive System Monitoring Approach for GNSS Activation2022Inngår i: IFIP Advances in Information and Communication Technology / [ed] Maglogiannis, I, Iliadis, L, Macintyre, J, Cortez, P, Springer Science+Business Media B.V., 2022, Vol. 647, s. 437-449Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.

  • 8.
    Adabala, Yashwanth Venkata Sai Kumar
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Devanaboina, Lakshmi Venkata Raghava Sudheer
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    A Prevention Technique for DDoS Attacks in SDN using Ryu Controller Application2024Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Software Defined Networking (SDN) modernizes network control, offering streamlined management. However, its centralized structure makes it more vulnerable to distributed Denial of Service (DDoS) attacks, posing serious threats to network stability. This thesis explores the development of a DDoS attack prevention technique in SDN environments using the Ryu controller application. The research aims to address the vulnerabilities in SDN, particularly focusing on flooding and Internet Protocol (IP) spoofing attacks, which are a significant threat to network security. The study employs an experimental approach, utilizing tools like Mininet-VM (VirtualMachine), Oracle VM VirtualBox, and hping3 to simulate a virtual SDN environment and conduct DDoS attack scenarios. Key methodologies include packet sniffing and rule-based detection by integrating Snort IDS (Intrusion Detection System), which is critical for identifying and mitigating such attacks. The experiments demonstrate the effectiveness of the proposed prevention technique, highlighting the importance of proper configuration and integration of network security tools in SDN. This work contributes to enhancing the resilience of SDN architectures against DDoS attacks, offering insights into future developments in network security. 

    Fulltekst (pdf)
    A_Prevention_Technique_for_DDoS_Attacks_in_SDN_using_Ryu_Controller_Application
  • 9.
    Adamov, Alexander
    et al.
    Kharkiv National University of Radio Electronics, UKR.
    Carlsson, Anders
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Reinforcement Learning for Anti-Ransomware Testing2020Inngår i: 2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2020, artikkel-id 9225141Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, we are going to verify the possibility to create a ransomware simulation that will use an arbitrary combination of known tactics and techniques to bypass an anti-malware defense. To verify this hypothesis, we conducted an experiment in which an agent was trained with the help of reinforcement learning to run the ransomware simulator in a way that can bypass anti-ransomware solution and encrypt the target files. The novelty of the proposed method lies in applying reinforcement learning to anti-ransomware testing that may help to identify weaknesses in the anti-ransomware defense and fix them before a real attack happens. © 2020 IEEE.

    Fulltekst (pdf)
    fulltext
  • 10.
    Adamov, Alexander
    et al.
    NioGuard Security Lab, UKR.
    Carlsson, Anders
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Surmacz, Tomasz
    Wrocław University of Science and Technology, POL.
    An analysis of lockergoga ransomware2019Inngår i: 2019 IEEE East-West Design and Test Symposium, EWDTS 2019, Institute of Electrical and Electronics Engineers Inc. , 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper contains an analysis of the LockerGoga ransomware that was used in the range of targeted cyberattacks in the first half of 2019 against Norsk Hydra-A world top 5 aluminum manufacturer, as well as the US chemical enterprises Hexion, and Momentive-Those companies are only the tip of the iceberg that reported the attack to the public. The ransomware was executed by attackers from inside a corporate network to encrypt the data on enterprise servers and, thus, taking down the information control systems. The intruders asked for a ransom to release a master key and decryption tool that can be used to decrypt the affected files. The purpose of the analysis is to find out tactics and techniques used by the LockerGoga ransomware during the cryptolocker attack as well as an encryption model to answer the question if the encrypted files can be decrypted with or without paying a ransom. The scientific novelty of the paper lies in an analysis methodology that is based on various reverse engineering techniques such as multi-process debugging and using open source code of a cryptographic library to find out a ransomware encryption model. © 2019 IEEE.

  • 11.
    Adeopatoye, Remilekun
    et al.
    Federal University of Technology, Nigeria.
    Ikuesan, Richard Adeyemi
    Zayed University, United Arab Emirates.
    Sookhak, Mehdi
    Texas A&m University, United States.
    Hungwe, Taurai
    Sefako Makgatho University of Health Sciences, South Africa.
    Kebande, Victor R.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Towards an Open-Source Based E-Mail Forensic Tool that uses Headers in Digital Investigation2023Inngår i: ACM International Conference Proceeding Series, ACM Digital Library, 2023Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Email-related incidents/crimes are on the rise owing to the fact that communication by electronic mail (e-mail) has become an important part of our daily lives. The technicality behind e-mail plays an important role when looking for digital evidence that can be used to create a hypothesis that can be used during litigation. During this process, it is needful to have a tool that can help to isolate email incidents as a potential crime scene in the wake of suspected attacks. The problem that this paper is addressing paper, is more centered on realizing an open-source email-forensic tool that used the header analysis approach. One advantage of this approach is that it helps investigators to collect digital evidence from e-mail systems, organize the collected data, analyze and discover any discrepancies in the header fields of an e-mail, and generates an evidence report. The main contribution of this paper focuses on generating a freshly computed hash that is attached to every generated report, to ensure the verifiability, reliability, and integrity of the reports to prove that they have not been modified in any way. Finally, this ensures that the sanctity and forensic soundness of the collected evidence are maintained. © 2023 ACM.

  • 12.
    Adurti, Devi Abhiseshu
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Battu, Mohit
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Optimization of Heterogeneous Parallel Computing Systems using Machine Learning2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    Background: Heterogeneous parallel computing systems utilize the combination of different resources CPUs and GPUs to achieve high performance and, reduced latency and energy consumption. Programming applications that target various processing units requires employing different tools and programming models/languages. Furthermore, selecting the most optimal implementation, which may either target different processing units (i.e. CPU or GPU) or implement the various algorithms, is not trivial for a given context. In this thesis, we investigate the use of machine learning to address the selection problem of various implementation variants for an application running on a heterogeneous system.

    Objectives: This study is focused on providing an approach for optimization of heterogeneous parallel computing systems at runtime by building the most efficient machine learning model to predict the optimal implementation variant of an application.

    Methods: The six machine learning models KNN, XGBoost, DTC, Random Forest Classifier, LightGBM, and SVM are trained and tested using stratified k-fold on the dataset generated from the matrix multiplication application for square matrix input dimension ranging from 16x16 to 10992x10992.

    Results: The results of each machine learning algorithm’s finding are presented through accuracy, confusion matrix, classification report for parameters precision, recall, and F-1 score, and a comparison between the machine learning models in terms of accuracy, run-time training, and run-time prediction are provided to determine the best model.

    Conclusions: The XGBoost, DTC, SVM algorithms achieved 100% accuracy. In comparison to the other machine learning models, the DTC is found to be the most suitable due to its low time required for training and prediction in predicting the optimal implementation variant of the heterogeneous system application. Hence the DTC is the best suitable algorithm for the optimization of heterogeneous parallel computing.

    Fulltekst (pdf)
    fulltext
  • 13.
    Ahlgren, Filip
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Local And Network Ransomware Detection Comparison2019Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    Background. Ransomware is a malicious application encrypting important files on a victim's computer. The ransomware will ask the victim for a ransom to be paid through cryptocurrency. After the system is encrypted there is virtually no way to decrypt the files other than using the encryption key that is bought from the attacker.

    Objectives. In this practical experiment, we will examine how machine learning can be used to detect ransomware on a local and network level. The results will be compared to see which one has a better performance.

    Methods. Data is collected through malware and goodware databases and then analyzed in a virtual environment to extract system information and network logs. Different machine learning classifiers will be built from the extracted features in order to detect the ransomware. The classifiers will go through a performance evaluation and be compared with each other to find which one has the best performance.

    Results. According to the tests, local detection was both more accurate and stable than network detection. The local classifiers had an average accuracy of 96% while the best network classifier had an average accuracy of 89.6%.

    Conclusions. In this case the results show that local detection has better performance than network detection. However, this can be because the network features were not specific enough for a network classifier. The network performance could have been better if the ransomware samples consisted of fewer families so better features could have been selected.

    Fulltekst (pdf)
    BTH2019Ahlgren
  • 14.
    Ahlstrand, Jim
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap. Telenor Sverige AB, Sweden..
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles2023Inngår i: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023 / [ed] Håkan Grahn, Anton Borg and Martin Boldt, Linköping University Electronic Press, 2023, s. 68-76Konferansepaper (Fagfellevurdert)
    Abstract [en]

    During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However,implementing and generating value from AI in the customerlife cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both themodel performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer lifecycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regardingoutcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating bothpolicy outputs and outcomes to ensure reliable and trustworthy AI.

    Fulltekst (pdf)
    fulltext
  • 15.
    Ahlström, Frida
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Karlsson, Janni
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Utvecklarens förutsättningar för säkerställande av tillgänglig webb2022Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [sv]

    Det har sedan 2019 varit lagkrav att offentliga webbplatser i Sverige skall uppfylla viss nivå av digital tillgänglighet. När den här studien publiceras ska ytterligare EU-direktiv bli nationell lag, vilket kommer att innebära att även privata aktörer berörs av motsvarande krav, däribland banktjänster och e-handeln. Detta kommer att innebära ökade krav som leverantörer och deras utvecklare behöver kunna möta. 

    Målen med studien är att skapa en medvetenhet om digital tillgänglighet och tydliggöra, utifrån utvecklarens perspektiv, hur man arbetar för att uppnå denna grad av tillgänglighet och vad som behövs för att mer effektivt tillämpa digital tillgänglighet. 

    För att åstadkomma detta har en kvalitativ intervjustudie genomförts. Totalt åtta intervjuer har genomförts, som sedan har transkriberats och tematiserats i resultatavsnittet. En induktiv tematisk analys är genomförd utifrån forskningsfrågorna. Den jämför tidigare resultat mot utfall från undersökningen och visar tydligt på likheter men även skillnader och nya upptäckter.

    Av undersökningen framgår att utvecklare har tillgång till utvärderingsverktyg och riktlinjer som ger ett gott stöd i arbetet, men att ansvaret ofta ligger på enskilda utvecklare snarare än på verksamheten som helhet. Detta är en av de största utmaningarna, tillsammans med att det fortfarande utvecklas otillgängligt parallellt och att tidspress gör att tillgänglighet kan prioriteras ned. Respondenterna är dock överens om att det inte tar längre tid att utveckla tillgängligt än otillgängligt, förutsatt att det tas i beaktande från början. Framgångsfaktorer i arbetet är att sälja in tillgänglighet till kunden, att arbeta strukturerat med kunskapsdelning och att dokumentera lösningar för att spara tid. Utöver detta framgår att tillgänglighetsfrågan skulle vinna på att ägarskapet lyfts till en högre beslutsnivå och kompetensen breddas i leverantörens organisation, samt att utvecklare får tillgång till specialistkompetens och användartester som stöd i arbetet. En grundkunskap om tillgänglighet skulle kunna inkluderas i webbutvecklingsutbildningar i större utsträckning, och en utökning av lagkraven skulle kunna skapa ytterligare incitament hos kunden. 

    Fulltekst (pdf)
    fulltext
  • 16.
    Ahmad, Al Ghaith
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Abd ULRAHMAN, Ibrahim
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Matching ESCF Prescribed Cyber Security Skills with the Swedish Job Market: Evaluating the Effectiveness of a Language Model2023Independent thesis Basic level (degree of Bachelor), 12 poäng / 18 hpOppgave
    Abstract [en]

    Background: As the demand for cybersecurity professionals continues to rise, it is crucial to identify the key skills necessary to thrive in this field. This research project sheds light on the cybersecurity skills landscape by analyzing the recommendations provided by the European Cybersecurity Skills Framework (ECSF), examining the most required skills in the Swedish job market, and investigating the common skills identified through the findings. The project utilizes the large language model, ChatGPT, to classify common cybersecurity skills and evaluate its accuracy compared to human classification.

    Objective: The primary objective of this research is to examine the alignment between the European Cybersecurity Skills Framework (ECSF) and the specific skill demands of the Swedish cybersecurity job market. This study aims to identify common skills and evaluate the effectiveness of a Language Model (ChatGPT) in categorizing jobs based on ECSF profiles. Additionally, it seeks to provide valuable insights for educational institutions and policymakers aiming to enhance workforce development in the cybersecurity sector.

    Methods: The research begins with a review of the European Cybersecurity Skills Framework (ECSF) to understand its recommendations and methodology for defining cybersecurity skills as well as delineating the cybersecurity profiles along with their corresponding key cybersecurity skills as outlined by ECSF. Subsequently, a Python-based web crawler, implemented to gather data on cybersecurity job announcements from the Swedish Employment Agency's website. This data is analyzed to identify the most frequently required cybersecurity skills sought by employers in Sweden. The Language Model (ChatGPT) is utilized to classify these positions according to ECSF profiles. Concurrently, two human agents manually categorize jobs to serve as a benchmark for evaluating the accuracy of the Language Model. This allows for a comprehensive assessment of its performance.

    Results: The study thoroughly reviews and cites the recommended skills outlined by the ECSF, offering a comprehensive European perspective on key cybersecurity skills (Tables 4 and 5). Additionally, it identifies the most in-demand skills in the Swedish job market, as illustrated in Figure 6. The research reveals the matching between ECSF-prescribed skills in different profiles and those sought after in the Swedish cybersecurity market. The skills of the profiles 'Cybersecurity Implementer' and 'Cybersecurity Architect' emerge as particularly critical, representing over 58% of the market demand. This research further highlights shared skills across various profiles (Table 7).

    Conclusion: This study highlights the matching between the European Cybersecurity Skills Framework (ECSF) recommendations and the evolving demands of the Swedish cybersecurity job market. Through a review of ECSF-prescribed skills and a thorough examination of the Swedish job landscape, this research identifies crucial areas of alignment. Significantly, the skills associated with 'Cybersecurity Implementer' and 'Cybersecurity Architect' profiles emerge as central, collectively constituting over 58% of market demand. This emphasizes the urgent need for educational programs to adapt and harmonize with industry requisites. Moreover, the study advances our understanding of the Language Model's effectiveness in job categorization. The findings hold significant implications for workforce development strategies and educational policies within the cybersecurity domain, underscoring the pivotal role of informed skills development in meeting the evolving needs of the cybersecurity workforce.

    Fulltekst (pdf)
    Matching ESCF Prescribed Cyber Security Skills with the Swedish Job Market: Evaluating the Effectiveness of a Language Model
  • 17.
    Ahmadi Mehri, Vida
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Towards Automated Context-aware Vulnerability Risk Management2023Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The information security landscape continually evolves with increasing publicly known vulnerabilities (e.g., 25064 new vulnerabilities in 2022). Vulnerabilities play a prominent role in all types of security related attacks, including ransomware and data breaches. Vulnerability Risk Management (VRM) is an essential cyber defense mechanism to eliminate or reduce attack surfaces in information technology. VRM is a continuous procedure of identification, classification, evaluation, and remediation of vulnerabilities. The traditional VRM procedure is time-consuming as classification, evaluation, and remediation require skills and knowledge of specific computer systems, software, network, and security policies. Activities requiring human input slow down the VRM process, increasing the risk of exploiting a vulnerability.

    The thesis introduces the Automated Context-aware Vulnerability Risk Management (ACVRM) methodology to improve VRM procedures by automating the entire VRM cycle and reducing the procedure time and experts' intervention. ACVRM focuses on the challenging stages (i.e., classification, evaluation, and remediation) of VRM to support security experts in promptly prioritizing and patching the vulnerabilities. 

    ACVRM concept is designed and implemented in a test environment for proof of concept. The efficiency of patch prioritization by ACVRM compared against a commercial vulnerability management tool (i.e., Rudder). ACVRM prioritized the vulnerability based on the patch score (i.e., the numeric representation of the vulnerability characteristic and the risk), the historical data, and dependencies. The experiments indicate that ACVRM could rank the vulnerabilities in the organization's context by weighting the criteria used in patch score calculation. The automated patch deployment is implemented with three use cases to investigate the impact of learning from historical events and dependencies on the success rate of the patch and human intervention. Our finding shows that ACVRM reduced the need for human actions, increased the ratio of successfully patched vulnerabilities, and decreased the cycle time of VRM process.

    Fulltekst (pdf)
    fulltext
  • 18.
    Ahmadi Mehri, Vida
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Towards Secure Collaborative AI Service Chains2019Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    At present, Artificial Intelligence (AI) systems have been adopted in many different domains such as healthcare, robotics, automotive, telecommunication systems, security, and finance for integrating intelligence in their services and applications. The intelligent personal assistant such as Siri and Alexa are examples of AI systems making an impact on our daily lives. Since many AI systems are data-driven systems, they require large volumes of data for training and validation, advanced algorithms, computing power and storage in their development process. Collaboration in the AI development process (AI engineering process) will reduce cost and time for the AI applications in the market. However, collaboration introduces the concern of privacy and piracy of intellectual properties, which can be caused by the actors who collaborate in the engineering process.  This work investigates the non-functional requirements, such as privacy and security, for enabling collaboration in AI service chains. It proposes an architectural design approach for collaborative AI engineering and explores the concept of the pipeline (service chain) for chaining AI functions. In order to enable controlled collaboration between AI artefacts in a pipeline, this work makes use of virtualisation technology to define and implement Virtual Premises (VPs), which act as protection wrappers for AI pipelines. A VP is a virtual policy enforcement point for a pipeline and requires access permission and authenticity for each element in a pipeline before the pipeline can be used.  Furthermore, the proposed architecture is evaluated in use-case approach that enables quick detection of design flaw during the initial stage of implementation. To evaluate the security level and compliance with security requirements, threat modeling was used to identify potential threats and vulnerabilities of the system and analyses their possible effects. The output of threat modeling was used to define countermeasure to threats related to unauthorised access and execution of AI artefacts.

    Fulltekst (pdf)
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  • 19.
    Ahmadi Mehri, Vida
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Arlos, Patrik
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Automated Context-Aware Vulnerability Risk Management for Patch Prioritization2022Inngår i: Electronics, E-ISSN 2079-9292, Vol. 11, nr 21, artikkel-id 3580Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The information-security landscape continuously evolves by discovering new vulnerabilities daily and sophisticated exploit tools. Vulnerability risk management (VRM) is the most crucial cyber defense to eliminate attack surfaces in IT environments. VRM is a cyclical practice of identifying, classifying, evaluating, and remediating vulnerabilities. The evaluation stage of VRM is neither automated nor cost-effective, as it demands great manual administrative efforts to prioritize the patch. Therefore, there is an urgent need to improve the VRM procedure by automating the entire VRM cycle in the context of a given organization. The authors propose automated context-aware VRM (ACVRM), to address the above challenges. This study defines the criteria to consider in the evaluation stage of ACVRM to prioritize the patching. Moreover, patch prioritization is customized in an organization’s context by allowing the organization to select the vulnerability management mode and weigh the selected criteria. Specifically, this study considers four vulnerability evaluation cases: (i) evaluation criteria are weighted homogeneously; (ii) attack complexity and availability are not considered important criteria; (iii) the security score is the only important criteria considered; and (iv) criteria are weighted based on the organization’s risk appetite. The result verifies the proposed solution’s efficiency compared with the Rudder vulnerability management tool (CVE-plugin). While Rudder produces a ranking independent from the scenario, ACVRM can sort vulnerabilities according to the organization’s criteria and context. Moreover, while Rudder randomly sorts vulnerabilities with the same patch score, ACVRM sorts them according to their age, giving a higher security score to older publicly known vulnerabilities. © 2022 by the authors.

    Fulltekst (pdf)
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  • 20.
    Ahmadi Mehri, Vida
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Arlos, Patrik
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Sapienza University of Rome, Italy.
    Automated Patch Management: An Empirical Evaluation Study2023Inngår i: Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023, IEEE, 2023, s. 321-328Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Vulnerability patch management is one of IT organizations' most complex issues due to the increasing number of publicly known vulnerabilities and explicit patch deadlines for compliance. Patch management requires human involvement in testing, deploying, and verifying the patch and its potential side effects. Hence, there is a need to automate the patch management procedure to keep the patch deadline with a limited number of available experts. This study proposed and implemented an automated patch management procedure to address mentioned challenges. The method also includes logic to automatically handle errors that might occur in patch deployment and verification. Moreover, the authors added an automated review step before patch management to adjust the patch prioritization list if multiple cumulative patches or dependencies are detected. The result indicated that our method reduced the need for human intervention, increased the ratio of successfully patched vulnerabilities, and decreased the execution time of vulnerability risk management.

    Fulltekst (pdf)
    fulltext
  • 21.
    Ahmadi Mehri, Vida
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Arlos, Patrik
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Normalization Framework for Vulnerability Risk Management in Cloud2021Inngår i: Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021, IEEE, 2021, s. 99-106Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Vulnerability Risk Management (VRM) is a critical element in cloud security that directly impacts cloud providers’ security assurance levels. Today, VRM is a challenging process because of the dramatic increase of known vulnerabilities (+26% in the last five years), and because it is even more dependent on the organization’s context. Moreover, the vulnerability’s severity score depends on the Vulnerability Database (VD) selected as a reference in VRM. All these factors introduce a new challenge for security specialists in evaluating and patching the vulnerabilities. This study provides a framework to improve the classification and evaluation phases in vulnerability risk management while using multiple vulnerability databases as a reference. Our solution normalizes the severity score of each vulnerability based on the selected security assurance level. The results of our study highlighted the role of the vulnerability databases in patch prioritization, showing the advantage of using multiple VDs.

    Fulltekst (pdf)
    fulltext
  • 22.
    Ahmadi Mehri, Vida
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap. City Network International AB, Sweden.
    Arlos, Patrik
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap. Sapienza University of Rome, ITA.
    Normalization of Severity Rating for Automated Context-aware Vulnerability Risk Management2020Inngår i: Proceedings - 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2020, Institute of Electrical and Electronics Engineers (IEEE), 2020, s. 200-205, artikkel-id 9196350Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In the last three years, the unprecedented increase in discovered vulnerabilities ranked with critical and high severity raise new challenges in Vulnerability Risk Management (VRM). Indeed, identifying, analyzing and remediating this high rate of vulnerabilities is labour intensive, especially for enterprises dealing with complex computing infrastructures such as Infrastructure-as-a-Service providers. Hence there is a demand for new criteria to prioritize vulnerabilities remediation and new automated/autonomic approaches to VRM.

    In this paper, we address the above challenge proposing an Automated Context-aware Vulnerability Risk Management (AC- VRM) methodology that aims: to reduce the labour intensive tasks of security experts; to prioritize vulnerability remediation on the basis of the organization context rather than risk severity only. The proposed solution considers multiple vulnerabilities databases to have a great coverage on known vulnerabilities and to determine the vulnerability rank. After the description of the new VRM methodology, we focus on the problem of obtaining a single vulnerability score by normalization and fusion of ranks obtained from multiple vulnerabilities databases. Our solution is a parametric normalization that accounts for organization needs/specifications.

    Fulltekst (pdf)
    fulltext
  • 23.
    Ahmed Sheik, Kareem
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    A Comparative Study on Optimization Algorithms and its efficiency2022Independent thesis Advanced level (degree of Master (Two Years)), 20 hpOppgave
    Abstract [en]

    Background: In computer science, optimization can be defined as finding the most cost-effective or notable achievable performance under certain circumstances, maximizing desired factors, and minimizing undesirable results. Many problems in the real world are continuous, and it isn't easy to find global solutions. However, computer technological development increases the speed of computations [1]. The optimization method, an efficient numerical simulator, and a realistic depiction of physical operations that we intend to describe and optimize for any optimization issue are all interconnected components of the optimization process [2].

    Objectives: A literature review on existing optimization algorithms is performed. Ten different benchmark functions are considered and are implemented on the existing chosen algorithms like GA (Genetic Algorithm), ACO (Ant ColonyOptimization) Method, and Plant Intelligence Behaviour optimization algorithm to measure the efficiency of these approaches based on the factors or metrics like CPU Time, Optimality, Accuracy, and Mean Best Standard Deviation.

    Methods: In this research work, a mixed-method approach is used. A literature review is performed based on the existing optimization algorithms. On the other hand, an experiment is conducted by using ten different benchmark functions with the current optimization algorithms like PSO algorithm, ACO algorithm, GA, and PIBO to measure their efficiency based on the four different factors like CPU Time, Optimality, Accuracy, Mean Best Standard Deviation. This tells us which optimization algorithms perform better.

    Results: The experiment findings are represented within this section. Using the standard functions on the suggested method and other methods, the various metrics like CPU Time, Optimality, Accuracy, and Mean Best Standard Deviation are considered, and the results are tabulated. Graphs are made using the data obtained.

    Analysis and Discussion: The research questions are addressed based on the experiment's results that have been conducted.

    Conclusion: We finally conclude the research by analyzing the existing optimization methods and the algorithms' performance. The PIBO performs much better and can be depicted from the results of the optimal metrics, best mean, standard deviation, and accuracy, and has a significant drawback of CPU Time where its time taken is much higher when compared to the PSO algorithm and almost close to GA and performs much better than ACO algorithm.

    Fulltekst (pdf)
    A Comparative Study on Optimization Algorithms and its efficiency
  • 24.
    Ahmed, Syed Saif
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Arepalli, Harshini Devi
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Auto-scaling Prediction using MachineLearning Algorithms: Analysing Performance and Feature Correlation2023Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Despite Covid-19’s drawbacks, it has recently contributed to highlighting the significance of cloud computing. The great majority of enterprises and organisations have shifted to a hybrid mode that enables users or workers to access their work environment from any location. This made it possible for businesses to save on-premises costs by moving their operations to the cloud. It has become essential to allocate resources effectively, especially through predictive auto-scaling. Although many algorithms have been studied regarding predictive auto-scaling, further analysis and validation need to be done. The objectives of this thesis are to implement machine-learning algorithms for predicting auto-scaling and to compare their performance on common grounds. The secondary objective is to find data connections amongst features within the dataset and evaluate their correlation coefficients. The methodology adopted for this thesis is experimentation. The selection of experimentation was made so that the auto-scaling algorithms can be tested in practical situations and compared to the results to identify the best algorithm using the selected metrics. This experiment can assist in determining whether the algorithms operate as predicted. Metrics such as Accuracy, F1-Score, Precision, Recall, Training Time andRoot Mean Square Error(RMSE) are calculated for the chosen algorithms RandomForest(RF), Logistic Regression, Support Vector Machine and Naive Bayes Classifier. The correlation coefficients of the features in the data are also measured, which helped in increasing the accuracy of the machine learning model. In conclusion, the features related to our target variable(CPU us-age, p95_scaling) often had high correlation coefficients compared to other features. The relationships between these variables could potentially be influenced by other variables that are unrelated to the target variable. Also, from the experimentation, it can be seen that the optimal algorithm for determining how cloud resources should be scaled is the Random Forest Classifier.

    Fulltekst (pdf)
    Auto-scaling Prediction using Machine Learning Algorithms: Analysing Performance and Feature Correlation
  • 25.
    Ajjapu, Siva Babu
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Lokireddy, Sasank Reddy
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Indoor VLC behaviour  with RGB spectral power distribution using simulation.2021Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    n recent years visible light communication (VLC) has been one of the technologies overgrowing in this competitive world and breaking through the wireless transmission of future mobile communications. This VLC replaces radio frequency (RF), which has several important features like large bandwidth, low cost, unlicensed spectrum. In telecommunications, there is a need for high bandwidth and secure transmission of data through a network. Communication can be done through wired and wireless. Wired communication such as coaxial cable, twisted wire, fiber optics, and wireless are RF, light fidelity (Li-Fi), optical wireless communication(OWC). In our daily lives, we are transferring data from one place to another through a network connection. The network is connected to multiple devices as the network bandwidth provided by VLC is higher than the RF communications. When multiple devices are connected to RF, the latency is high. In the case of VLC, the latency is low. In this research, the light emitting diode (LED) bulbs act as the transmitter(Tx), and the avalanche photodiode (APD) acts as a receiver(Rx).

    This research mainly focuses on creating a MATLAB simulation environment for a two-room VLC system with given spectral power distributions. We have simulated two rooms with the exact dimensions. The LEDs are placed in opposite positions in each room. LED is placed at the middle top of the ceiling in one room, and a photodiode (PD) is placed on top of the table under the light in the same room. Moreover, in another room, the light is placed on top of the table at the bottom, and PD is placed at the middle top of the ceiling.Moreover, these two rooms are connected to the same network.   The input parameters are taken from the previous studies, but the transmitting power is calculated from the Red-Green-Blue(RGB or White) light spectrum distribution using the OOK modulation technique. We obtained responsivity of APD at a single point and bit error rates(BER) of APD at multi-points inside both the rooms.

    Fulltekst (pdf)
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  • 26.
    Aklilu, Yohannes T.
    et al.
    University of Skövde, SWE.
    Ding, Jianguo
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Survey on blockchain for smart grid management, control, and operation2022Inngår i: Energies, E-ISSN 1996-1073, Vol. 15, nr 1, artikkel-id 193Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Power generation, distribution, transmission, and consumption face ongoing challenges such as smart grid management, control, and operation, resulting from high energy demand, the diversity of energy sources, and environmental or regulatory issues. This paper provides a comprehensive overview of blockchain-based solutions for smart grid management, control, and operations. We systematically summarize existing work on the use and implementation of blockchain technology in various smart grid domains. The paper compares related reviews and highlights the challenges in the management, control, and operation for a blockchain-based smart grid as well as future research directions in the five categories: collaboration among stakeholders; data analysis and data manage-ment; control of grid imbalances; decentralization of grid management and operations; and security and privacy. All these aspects have not been covered in previous reviews. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

    Fulltekst (pdf)
    fulltext
  • 27.
    AKULA, SAI PANKAJ
    Blekinge Tekniska Högskola, Enheten för utbildningsutveckling. Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    A critical evaluation on SRK STORE APP by using the Heuristic Principles of Usability2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    This thesis aim is to do a critical evaluation on SRK STORE APP (Shopping app for android) by applying the heuristic principles of usability, to obtain the usability issues or problems for the respective mobile application. Another vital element of this thesis is to attain the necessary suggestions for the respective mobile application by accomplishing the principles of the heuristic evaluation required for the mobile application. On the other hand, the outcome should be demonstrated that the mobile application is user flexible by following the principles of heuristic.

    Background: To be aesthetic and attractive, the mobile application should be given an ideal user experience with usability. So we decided to focus on this utility field and while looking through the different articles, we came across one that talks about design principles and their concepts. The current thesis idea has been obtained by the literature survey we have done on the design principles of the heuristic evaluation and its concepts. This thesis is to attain the necessary suggestions and as well as the complemented solutions/recommendations for the specific mobile application by accomplishing the principles of the heuristic evaluation required for the mobile application.

    Objectives: The main objectives of this project are examining the design principles and identifying the usability issues or problems of the respective mobile application and anthologizing a list of necessary suggestions for enhancing the mobile application and providing absolute recommendations to the existing application.

    Methods: To compile a list of necessary suggestions and providing absolute recommendations for the mobile application, we have applied Jakob Neilson’s design principles. This specific method aids in determining the utility of design criteria and aids in the transformation of the interactive system by analyzing factors such as usability. Using this method, we will provide a concise detailed overview of the importance of design principles in an interaction. The key aim of employing design principles of usability is to ensure the performance and reliability of the effective interaction design, to provide meaningful user interaction assistance, and as well as to dispense an acceptable and optimal user experience.

    Results: The results here obtained are the usability issues of the respective mobile application i.e., SRK STORE APP, and the heuristic principles which are not satisfied by the specific mobile application. The severity level of the heuristic principles will have resulted and the list of necessary suggestions for enhancing the mobile application and providing absolute recommendations to the existing application.

    Conclusions: This study was conducted to evaluate the mobile application. Heuristic evaluation methodology was used to evaluate the system. Jakob Neilson’sdesign principles were used to depict the usability issues of the mobile application. The required suggestions and absolute recommendations/solutions are provided to the existing mobile application. 

    Fulltekst (pdf)
    A critical evaluation on SRK STORE APP by using the Heuristic Principles of Usability
  • 28.
    Akurathi, Lakshmikanth
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Chilluguri, Surya Teja Reddy
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Decode and Forward Relay Assisting Active Jamming in NOMA System2022Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Non-orthogonal multiple access (NOMA), with its exceptional spectrum efficiency, was thought to be a promising technology for upcoming wireless communications. Physical layer security has also been investigated to improve the security performance of the system. Power-domain NOMA has been considered for this paper, where multiple users can share the same spectrum which bases this sharing on distinct power values. Power allocation is used to allocate different power to the users based on their channel condition. Data signals of different users are superimposed on the transmitter's side, and the receiver uses successive interference cancellation (SIC) to remove the unwanted signals before decoding its own signal. There exist an eavesdropper whose motive is to eavesdrop on the confidential information that is being shared with the users. The network model developed in this way consists of two links, one of which considers the relay transmission path from the source to Near User to Far User and the other of which takes into account the direct transmission path from the source to the destination, both of which experience Nakagami-m fading. To degrade the eavesdropper's channel, the jamming technique is used against the eavesdropper where users are assumed to be in a full-duplex mode which aims to improve the security of the physical layer. Secrecy performance metrics such as secrecy outage probability, secrecy capacity, etc. are evaluated and analyzed for the considered system. Mathematical analysis and simulation using MATLAB are done to assess, analyze and visualize the system's performance in the presence of an eavesdropper when the jamming technique is applied. According to simulation results, the active jamming approach enhances the secrecy performance of the entire system and leads to a positive improvement in the secrecy rate.

    Fulltekst (pdf)
    Decode and Forward Relay Assisting Active Jamming in NOMA System
  • 29.
    Alanko Öberg, John
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Svensson, Carl
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Time-based Key for Coverless Audio Steganography: A Proposed Behavioral Method to Increase Capacity2023Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Background. Coverless steganography is a relatively unexplored area of steganography where the message is not embedded into a cover media. Instead the message is derived from one or several properties already existing in the carrier media. This renders steganalysis methods used for traditional steganography useless. Early coverless methods were applied to images or texts but more recently the possibilities in the video and audio domain have been explored. The audio domain still remains relatively unexplored however, with the earliest work being presented in 2022. In this thesis, we narrow the existing research gap by proposing an audio-compatible method which uses the timestamp that marks when a carrier media was received to generate a time-based key which can be applied to the hash produced by said carrier. This effectively allows one carrier to represent a range of different hashes depending on the timestamp specifying when it was received, increasing capacity.

    Objectives. The objectives of the thesis are to explore what features of audio are suitable for steganographic use, to establish a method for finding audio clips which can represent a specific message to be sent and to improve on the current state-of-the-art method, taking capacity, robustness and cost into consideration.

    Methods. A literature review was first conducted to gain insight on techniques used in previous works. This served both to illuminate features of audio that could be used to good effect in a coverless approach, and to identify coverless approaches which could work but had not been tested yet. Experiments were then performed on two datasets to show the effective capacity increase of the proposed method when used in tandem with the existing state-of-the-art method for coverless audio steganography. Additional robustness tests for said state-of-the-art method were also performed.

    Results. The results show that the proposed method could increase the per-message capacity from eight bits to 16 bits, while still retaining 100% effective capacity using only 200 key permutations, given a database consisting of 50 one-minute long audio clips. They further show that the time cost added by the proposed method is in total less than 0.1 seconds for 2048 key permutations. The robustness experiments show that the hashing algorithms used in the state-of-the-art method have high robustness against additive white gaussian noise, low-pass filters, and resampling attacks but are weaker against compression and band-pass filters. 

    Conclusions. We address the scientific gap and complete our objectives by proposing a method which can increase capacity of existing coverless steganography methods. We demonstrate the capacity increase our method brings by using it in tandem with the state-of-the-art method for the coverless audio domain. We argue that our method is not limited to the audio domain, or to the coverless method with which we performed our experiments. Finally, we discuss several directions for future works. 

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    fulltext
  • 30.
    Alawadi, Sadi
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Alkharabsheh, Khalid
    Al-Balqa Applied University, Jordan.
    Alkhabbas, Fahed
    Malmö University, Internet of Things and People Research Center.
    Kebande, Victor R.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Awaysheh, Feras M.
    Institute of Computer Science, Estonia.
    Palomba, Fabio
    University of Salerno, Italy.
    Awad, Mohammed
    Arab American University, Palestine.
    FedCSD: A Federated Learning Based Approach for Code-Smell Detection2024Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 12, s. 44888-44904Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Software quality is critical, as low quality, or 'Code smell,' increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting 'God Class,' to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model's highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers. © 2013 IEEE.

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  • 31.
    Al-Dhaqm, Arafat
    et al.
    Univ Teknol Malaysia UTM, MYS.
    Ikuesan, Richard Adeyemi
    Community Coll Qatar, QAT.
    Kebande, Victor R.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Abd Razak, Shukor
    Univ Teknol Malaysia UTM, MYS.
    Grispos, George
    Univ Nebraska, USA.
    Choo, Kim-Kwang Raymond
    Univ Texas San Antonio, USA.
    Al-Rimy, Bander Ali Saleh
    Univ Teknol Malaysia UTM, MYS.
    Alsewari, Abdulrahman A.
    Univ Malaysia Pahang, MYS.
    Digital Forensics Subdomains: The State of the Art and Future Directions2021Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 9, s. 152476-152502Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    For reliable digital evidence to be admitted in a court of law, it is important to apply scientifically proven digital forensic investigation techniques to corroborate a suspected security incident. Mainly, traditional digital forensics techniques focus on computer desktops and servers. However, recent advances in digital media and platforms have seen an increased need for the application of digital forensic investigation techniques to other subdomains. This includes mobile devices, databases, networks, cloud-based platforms, and the Internet of Things (IoT) at large. To assist forensic investigators to conduct investigations within these subdomains, academic researchers have attempted to develop several investigative processes. However, many of these processes are domain-specific or describe domain-specific investigative tools. Hence, in this paper, we hypothesize that the literature is saturated with ambiguities. To further synthesize this hypothesis, a digital forensic model-orientated Systematic Literature Review (SLR) within the digital forensic subdomains has been undertaken. The purpose of this SLR is to identify the different and heterogeneous practices that have emerged within the specific digital forensics subdomains. A key finding from this review is that there are process redundancies and a high degree of ambiguity among investigative processes in the various subdomains. As a way forward, this study proposes a high-level abstract metamodel, which combines the common investigation processes, activities, techniques, and tasks for digital forensics subdomains. Using the proposed solution, an investigator can effectively organize the knowledge process for digital investigation.

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  • 32.
    Alkhabbas, Fahed
    et al.
    Malmö University.
    Alawadi, Sadi
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Ayyad, Majed
    Birzeit University, Palestine.
    Spalazzese, Romina
    Malmö University.
    Davidsson, Paul
    Malmö University.
    ART4FL: An Agent-based Architectural Approach for Trustworthy Federated Learning in the IoT2023Inngår i: 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023 / [ed] Quwaider M., Awaysheh F.M., Jararweh Y., Institute of Electrical and Electronics Engineers (IEEE), 2023, s. 270-275Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has opened up for the development of novel types of systems and services. Federated Learning (FL) has enabled the systems to collaboratively train their ML models while preserving the privacy of the data collected by their IoT devices and objects. Several FL frameworks have been developed, however, they do not enable FL in open, distributed, and heterogeneous IoT environments. Specifically, they do not support systems that collect similar data to dynamically discover each other, communicate, and negotiate about the training terms (e.g., accuracy, communication latency, and cost). Towards bridging this gap, we propose ART4FL, an end-to-end framework that enables FL in open IoT settings. The framework enables systems’ users to configure agents that participate in FL on their behalf. Those agents negotiate and make commitments (i.e., contractual agreements) to dynamically form federations. To perform FL, the framework deploys the needed services dynamically, monitors the training rounds, and calculates agents’ trust scores based on the established commitments. ART4FL exploits a blockchain network to maintain the trust scores, and it provides those scores to negotiating agents’ during the federations’ formation phase. © 2023 IEEE.

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  • 33.
    Alkhabbas, Fahed
    et al.
    Malmö University, SWE.
    Alsadi, Mohammed
    Norwegian University of Science and Technology, NOR.
    Alawadi, Sadi
    Uppsala University, SWE.
    Awaysheh, Feras M.
    University of Tartu, EST.
    Kebande, Victor R.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Moghaddam, Mahyar T.
    University of Southern Denmark, DEN.
    ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems2022Inngår i: Sensors, E-ISSN 1424-8220, Vol. 22, nr 18, artikkel-id 6842Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors.

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  • 34.
    Alkharabsheh, Khalid
    et al.
    Al-Balqa Applied University, JOR.
    Alawadi, Sadi
    Uppsala University, SWE.
    Kebande, Victor R.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Crespo, Yania
    Universidad de Valladolid, ESP.
    Fernández-Delgado, Manuel
    Universidad de Santiago de Compostela, ESP.
    Taboada, José A.
    Universidad de Santiago de Compostela, ESP.
    A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class2022Inngår i: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 143, artikkel-id 106736Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, such as Kappa, ROC, and MCC, have been used in the assessment of the classifier behavior. © 2021 The Authors

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  • 35.
    Alladi, Sai Sumeeth
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Prioritized Database Synchronization using Optimization Algorithms2023Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Fulltekst (pdf)
    Prioritized Database Synchronization using Optimization Algorithms
  • 36.
    Alluri, Gayathri Thanuja
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Performance Evaluation of Apache Cassandra using AWS (Amazon Web Services) and GCP (Google Cloud Platform)2022Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Context: In the field of computer science and communication systems, cloud computing plays animportant role in Information and Technology industry, it allows users to start from small and increase resources when there is a demand. AWS (Amazon Web Services) and GCP (Google cloud Platform) are two different cloud platform providers. Many organizations are still relying onstructured databases like MySQL etc. Structured databases cannot handle huge requests and data efficiently when number of requests and data increase. To overcome this problem, the organizations shift to NoSQL unstructured databases like Apache cassandra, Mongo DB etc.

    Conclusions: From the literature review, I have gained knowledge regarding the cloud computing, problems existed in cloud, which leads to setup this research in evaluating the performance of cassandra on AWS and GCP. The conclusion from the experiment is that as the thread count increases throughput and latency has increased gradually till thread count 600 in both the clouds. By comparing both the clouds throughput values, AWS scales up compare to GCP. GCP scales up, when compared to AWS in terms of latency. 

    Keywords: Apache Cassandra, AWS, Google Cloud Platform, Cassandra Stress, Throughput, Latency

    Fulltekst (pdf)
    Performance Evaluation of Apache Cassandra using AWS (Amazon Web Services) and GCP (Google Cloud Platform)
  • 37.
    Al-Saedi, Ahmed Abbas Mohsin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Resource-Aware and Personalized Federated Learning via Clustering Analysis2024Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Today’s advancement in Artificial Intelligence (AI) enables training Machine Learning (ML) models on the daily-produced data by connected edge devices. To make the most of the data stored on the device, conventional ML approaches require gathering all individual data sets and transferring them to a central location to train a common model. However, centralizing data incurs significant costs related to communication, network resource utilization, high volume of traffic, and privacy issues. To address the aforementioned challenges, Federated Learning (FL) is employed as a novel approach to train a shared model on decentralized edge devices while preserving privacy. Despite the significant potential of FL, it still requires considerable resources such as time, computational power, energy, and bandwidth availability. More importantly, the computational capabilities of the training devices may vary over time. Furthermore, the devices involved in the training process of FL may have distinct training datasets that differ in terms of their size and distribution. As a result of this, the convergence of the FL models may become unstable and slow. These differences can influence the FL process and ultimately lead to suboptimal model performance within a heterogeneous federated network.

    In this thesis, we have tackled several of the aforementioned challenges. Initially, a FL algorithm is proposed that utilizes cluster analysis to address the problem of communication overhead. This issue poses a major bottleneck in FL, particularly for complex models, large-scale applications, and frequent updates. The next research conducted in this thesis involved extending the previous study to include wireless networks (WNs). In WSNs, achieving energy-efficient transmission is a significant challenge due to their limited resources. This has motivated us to continue with a comprehensive overview and classification of the latest advancements in context-aware edge-based AI models, with a specific emphasis on sensor networks. The review has also investigated the associated challenges and motivations for adopting AI techniques, along with an evaluation of current areas of research that need further investigation. To optimize the aggregation of the FL model and alleviate communication expenses, the initial study addressing communication overhead is extended to include a FL-based cluster optimization approach. Furthermore, to reduce the detrimental effect caused by data heterogeneity among edge devices on FL, a new study of group-personalized FL models has been conducted. Finally, taking inspiration from the previously mentioned FL models, techniques for assessing clients' contribution by monitoring and evaluating their behavior during training are proposed. In comparison with the most existing contribution evaluation solutions, the proposed techniques do not require significant computational resources.

    The FL algorithms presented in this thesis are assessed on a range of real-world datasets. The extensive experiments demonstrated that the proposed FL techniques are effective and robust. These techniques improve communication efficiency, resource utilization, model convergence speed, and aggregation efficiency, and also reduce data heterogeneity when compared to other state-of-the-art methods.

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  • 38.
    Al-Saedi, Ahmed Abbas Mohsin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Group-Personalized Federated Learning for Human Activity Recognition Through Cluster Eccentricity Analysis2023Inngår i: Engineering Applications of Neural Networks: 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings / [ed] Iliadis L., Maglogiannis I., Alonso S., Jayne C., Pimenidis E., Springer Science+Business Media B.V., 2023, s. 505-519Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Human Activity Recognition (HAR) plays a significant role in recent years due to its applications in various fields including health care and well-being. Traditional centralized methods reach very high recognition rates, but they incur privacy and scalability issues. Federated learning (FL) is a leading distributed machine learning (ML) paradigm, to train a global model collaboratively on distributed data in a privacy-preserving manner. However, for HAR scenarios, the existing action recognition system mainly focuses on a unified model, i.e. it does not provide users with personalized recognition of activities. Furthermore, the heterogeneity of data across user devices can lead to degraded performance of traditional FL models in the smart applications such as personalized health care. To this end, we propose a novel federated learning model that tries to cope with a statistically heterogeneous federated learning environment by introducing a group-personalized FL (GP-FL) solution. The proposed GP-FL algorithm builds several global ML models, each one trained iteratively on a dynamic group of clients with homogeneous class probability estimations. The performance of the proposed FL scheme is studied and evaluated on real-world HAR data. The evaluation results demonstrate that our approach has advantages in terms of model performance and convergence speed with respect to two baseline FL algorithms used for comparison. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

  • 39.
    Al-Saedi, Ahmed Abbas Mohsin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Contribution Prediction in Federated Learning via Client Behavior EvaluationManuskript (preprint) (Annet vitenskapelig)
  • 40.
    Al-Saedi, Ahmed Abbas Mohsin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    FedCO: Communication-Efficient Federated Learning via Clustering Optimization †2022Inngår i: Future Internet, E-ISSN 1999-5903, Vol. 14, nr 12, artikkel-id 377Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To address this challenge, we propose a clustering-based federated solution, entitled Federated Learning via Clustering Optimization (FedCO), which optimizes model aggregation and reduces communication costs. In order to reduce the communication costs, we first divide the participating workers into groups based on the similarity of their model parameters and then select only one representative, the best performing worker, from each group to communicate with the central server. Then, in each successive round, we apply the Silhouette validation technique to check whether each representative is still made tight with its current cluster. If not, the representative is either moved into a more appropriate cluster or forms a cluster singleton. Finally, we use split optimization to update and improve the whole clustering solution. The updated clustering is used to select new cluster representatives. In that way, the proposed FedCO approach updates clusters by repeatedly evaluating and splitting clusters if doing so is necessary to improve the workers’ partitioning. The potential of the proposed method is demonstrated on publicly available datasets and LEAF datasets under the IID and Non-IID data distribution settings. The experimental results indicate that our proposed FedCO approach is superior to the state-of-the-art FL approaches, i.e., FedAvg, FedProx, and CMFL, in reducing communication costs and achieving a better accuracy in both the IID and Non-IID cases. © 2022 by the authors.

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  • 41.
    Al-Saedi, Ahmed Abbas Mohsin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Reducing Communication Overhead of Federated Learning through Clustering Analysis2021Inngår i: 26th IEEE Symposium on Computers and Communications (ISCC 2021), Institute of Electrical and Electronics Engineers (IEEE), 2021Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Training of machine learning models in a Datacenter, with data originated from edge nodes, incurs high communication overheads and violates a user's privacy. These challenges may be tackled by employing Federated Learning (FL) machine learning technique to train a model across multiple decentralized edge devices (workers) using local data. In this paper, we explore an approach that identifies the most representative updates made by workers and those are only uploaded to the central server for reducing network communication costs. Based on this idea, we propose a FL model that can mitigate communication overheads via clustering analysis of the worker local updates. The Cluster Analysis-based Federated Learning (CA-FL) model is studied and evaluated in human activity recognition (HAR) datasets. Our evaluation results show the robustness of CA-FL in comparison with traditional FL in terms of accuracy and communication costs on both IID and non-IID  cases.

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  • 42.
    Al-Saedi, Ahmed Abbas Mohsin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Exner, Peter
    Sony, R&D Center Europe, SWE.
    Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview2022Inngår i: Sensors, E-ISSN 1424-8220, Vol. 22, nr 15, artikkel-id 5544Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

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  • 43.
    Al-Saedi, Ahmed Abbas Mohsin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    An Energy-aware Multi-Criteria Federated Learning Model for Edge Computing2021Inngår i: Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021 / [ed] Younas M., Awan I., Unal P., IEEE, 2021, s. 134-143Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The successful convergence of Internet of Things (IoT) technology and distributed machine learning have leveraged to realise the concept of Federated Learning (FL) with the collaborative efforts of a large number of low-powered and small-sized edge nodes. In Wireless Networks (WN), an energy-efficient transmission is a fundamental challenge since the energy resource of edge nodes is restricted.In this paper, we propose an Energy-aware Multi-Criteria Federated Learning (EaMC-FL) model for edge computing. The proposed model enables to collaboratively train a shared global model by aggregating locally trained models in selected representative edge nodes (workers). The involved workers are initially partitioned into a number of clusters with respect to the similarity of their local model parameters. At each training round a small set of representative workers is selected on the based of multi-criteria evaluation that scores each node representativeness (importance) by taking into account the trade-off among the node local model performance, consumed energy and battery lifetime. We have demonstrated through experimental results the proposed EaMC-FL model is capable of reducing the energy consumed by the edge nodes by lowering the transmitted data.

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  • 44.
    Al-Shuwaili, Mustafa
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för maskinteknik.
    Helo, Zeid
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Optimering av interna materialflödet på Scandinavian Stone2020Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [sv]

    Examensarbetet fokuserar på analysering och utveckling av ett materialflöde på ett stenbrott, som tillhör företaget Scandinavian Stone. Ett materialflöde som är beroende av tunga fordon, som orsakar höga kostnader och koldioxidutsläpp. Företaget strävar efter en utveckling som fokuserar på koldioxidutsläpp – och kostnadsreducering. Processen undersöktes vid flera aktiviteter, som studiebesök, intervjuer och observationer. En undersökning är grunden till en processanalys som tar till ytan utvecklingsbehoven och bristfaktorer. Analysen grundades på Lean-filosofin, för att särskilja mellan värdehöjande och icke värdehöjande parametrar ur ett kundperspektiv. Resultatet av analysen visade att sträckan mellan lägsta punkten i stenbrottet (hålet) till bearbetningsstation, är den delen som bidrar med högst energiförbrukning. En beräkningsmodell har skapats för att kunna räkna på energiåtgången som är resulterad av produkttransporter inom processen. Beräkningen gjordes på en förenklad körcykel med varierade produktvikter. Resultatet från beräkningsmodellen visade att en ökning på produktvikten medför små energiökningar. Detta var grunden till ett koncept som fokuserar på en minskning av antal körningar längs den identifierade sträckan. För att bibehålla processproduktivitet, krävs det att transportera flera produkter och tyngre skrotmaterial åt gången. Det förslagna konceptet ändrar layouten på processen, då bearbetningsstation flyttas ned till hålet, istället för sin nuvarande plats utanför hålet. Det möjliggör transport av flera produkter åt gången, då produkterna förlorar ca 50 % av sin vikt efter bearbetning. Restmaterialet förvaras tillfälligt i en behållare och sedan transporteras upp när vikten har nått en maximalnivå. Denna maximalvikt observeras med hjälp av en våg som behållaren är placerad på. Vågen indikerar när den gränsen är uppnådd och det är dags för tömning. Konceptet visade en lönsamhet i energiförbrukningen i upp till 40%.

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  • 45.
    Alsolai, Hadeel
    et al.
    Princess Nourah Bint Abdulrahman Univ, SAU.
    Qureshi, Shahnawaz
    Natl Univ Comp & Emerging Sci, PAK.
    Iqbal, Syed Muhammad Zeeshan
    BrightWare LLC, SAU.
    Ameer, Asif
    Natl Univ Comp & Emerging Sci, PAK.
    Cheaha, Dania
    Prince Songkla Univ, THA.
    Henesey, Lawrence
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Karrila, Seppo
    Prince Songkla Univ, THA.
    Employing a Long-Short-Term Memory Neural Network to Improve Automatic Sleep Stage Classification of Pharmaco-EEG Profiles2022Inngår i: Applied Sciences, E-ISSN 2076-3417, Vol. 12, nr 10, artikkel-id 5248Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An increasing problem in today's society is the spiraling number of people suffering from various sleep disorders. The research results presented in this paper support the use of a novel method that employs techniques from the classification of sleep disorders for more accurate scoring. Applying this novel method will assist researchers with better analyzing subject profiles for recommending prescriptions or to alleviate sleep disorders. In biomedical research, the use of animal models is required to experimentally test the safety and efficacy of a drug in the pre-clinical stage. We have developed a novel LSTM Recurrent Neural Network to process Pharmaco-EEG Profiles of rats to automatically score their sleep-wake stages. The results indicate improvements over the current methods; for the case of combined channels, the model accuracy improved by 1% and 3% in binary or multiclass classifications, respectively, to accuracies of 93% and 82%. In the case of using a single channel, binary and multiclass LSTM models for identifying rodent sleep stages using single or multiple electrode positions for binary or multiclass problems have not been evaluated in prior literature. The results reveal that single or combined channels, and binary or multiclass classification tasks, can be applied in the automatic sleep scoring of rodents.

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  • 46.
    Alsolai, Hadeel
    et al.
    Princess Nourah bint Abdulrahman University, SAU.
    Qureshi, Shahnawaz
    National University of Computing and Emerging Sciences, PAK.
    Iqbal, Syed Muhammad Zeeshan
    Research and Development, BrightWare LLC, SAU.
    Vanichayobon, Sirirut
    Prince of Songkla University, THA.
    Henesey, Lawrence
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Lindley, Craig
    CSIRO Data, AUS.
    Karrila, Seppo
    Prince of Songkla University, THA.
    A Systematic Review of Literature on Automated Sleep Scoring2022Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 10, s. 79419-79443Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

    Fulltekst (pdf)
    fulltext
  • 47.
    Andersson, Johan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Employing gamification to enhance the engagement of video education2022Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
  • 48.
    Andersson, Jonathan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap. 9705#.
    Effects of Menu Systems, Interaction Methods, and Posture on User Experience in Virtual Reality2023Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Background. In recent years, Virtual Reality (VR) has emerged as an important technology in both commercial and industrial use. This has prompted large investments from large corporations, and some have even shifted their focus toward this new rising technology. With the oncoming of this tech as mainstream, emphasis has been put on the content itself, while the surrounding user experiences of the UIsand the interaction methods in the VR environment have been put aside.

    Objectives. The objectives of this thesis are to explore different menu systems together with interaction methods while also evaluating their effect of them and the posture of the user on user experience and simulator sickness in VR applications. Data collected could provide good observations for how menus and interaction methods together with posture can be best designed for VR applications.

    Methods. A VR application with two different menu systems, and two different interaction methods were implemented, and a survey based on the System UsabilityScale (SUS), After-Scenario Questionnaire (ASQ), and Simulator Sickness Questionnaire (SSQ) was created. These questionnaires answer matters relating to user experience and cybersickness and were chosen for their ease of use in addition to being used in similar works. Together these formed the basis for an experiment which was carried out with 20 participants. The study measured the differences in user experience, time taken, and simulator sickness for the different combinations of controls, menus, and postures.

    Results. Results show that there are significant differences depending on the controls, menu systems, and posture in both user experience and simulator sickness. The study showed that participants reported fewer simulator sickness symptoms when seated and that the overall best control and menu combination was a traditional panel menu together with motion controls.

    Conclusions. Among the options explored in the study, traditional, top-down, panel menus together with motion controls form the best combination in regard to the user experience in VR applications. A sitting posture provides the overall best environment in VR applications in regard to less severe simulator sickness symptoms

    Fulltekst (pdf)
    Effects of Menu Systems, Interaction Methods, and Posture on User Experience in Virtual Reality
  • 49.
    Andersson, Jonathan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Using Gamification to Improve User Experience and Health Effects in Mobile Applications2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    Background. According to the World Health Organization, over 264 million people suffer from depression. A recent trend to treat and combat depression is e-health applications like Headspace with the help of mindfulness or meditation. The rise of new treatment methods based on these concepts are seen as a promising alternative to traditional methods like cognitive behavioural therapy and medication. Objectives. The objectives of this study is to make a new mobile application, in the form of a mobile e-health prototype. The application, called MindBud, is designed to help the user reduce depressive thoughts. This is done by using a daily schedule to plan your day and in turn, reduce depressive thoughts and procrastination through structure. Then, the study seeks to compare two versions of this application, one version will have gamification elements and one will be without them. The comparison will measure overall user experience through a test called the system usability scale, and in addition measure the effectiveness of the application on depressive thoughts.Methods. Two versions of MindBud were implemented, one basic app and one with gamification elements added to it. The applications were then tested by performing an experiment with sixteen participants. Each of the participants tested both versions of the application, and then answered a questionnaire about the app. The answers of the questionnaire were used to compare test scores between the two versions of the application, to see if gamification had any impact on overall user experience and to see which gamification elements could be used to reduce depressive thoughts through the application. Results. The results show a slight increase in score in regards to overall user experience when comparing the gamified app with the basic one. Most notable increases came in questions about frequency of use, and complexity of the application. Additionally, the gamified application scored significantly better when participants were asked how much they thought the app version would reduce depressive thoughts.Conclusions. The gamification elements added were found to increase overall user experience, and also help reduce depressive thoughts more than the basic version. The used gamification elements were an in-game avatar, a reward system and an experience and level system. 

    Fulltekst (pdf)
    fulltext
  • 50.
    Andersson, Jonathan
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap. student.
    Hu, Yan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Exploring the Impact of Menu Systems, Interaction Methods, and Sitting or Standing Posture on User Experience in Virtual Reality2023Inngår i: 2023 IEEE Gaming, Entertainment, and Media Conference, GEM 2023 / [ed] Gittens C., Hogue A., Cannavo A., Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Virtual Reality (VR) has become an increasingly crucial aspect in both commercial and industrial settings. However, the user experience of the user interfaces and interaction methods in the VR environment is often overlooked. This paper aims to explore different menu systems, interaction methods, and the user’s sitting or standing posture on user experience and cybersickness in VR applications. An experiment with two menu systems and two interaction methods in an implemented VR application was conducted with 20 participants. The results found that traditional, top-down, panel menus with motion controls are the best combination regarding the user experience. Sitting posture provides less severe simulator sickness symptoms than standing.

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