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Casalicchio, EmilianoORCID iD iconorcid.org/0000-0002-3118-5058
Publications (10 of 29) Show all publications
Sundstedt, V., Boeva, V., Zepernick, H.-J., Goswami, P., Cheddad, A., Tutschku, K., . . . Arlos, P. (2023). HINTS: Human-Centered Intelligent Realities. In: Håkan Grahn, Anton Borg and Martin Boldt (Ed.), 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023: . Paper presented at 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023 (pp. 9-17). Linköping University Electronic Press
Open this publication in new window or tab >>HINTS: Human-Centered Intelligent Realities
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2023 (English)In: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023 / [ed] Håkan Grahn, Anton Borg and Martin Boldt, Linköping University Electronic Press, 2023, p. 9-17Conference paper, Published paper (Refereed)
Abstract [en]

During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors   

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2023
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 199
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:bth-25413 (URN)10.3384/ecp199001 (DOI)9789180752749 (ISBN)
Conference
35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023
Funder
Knowledge Foundation, 20220068
Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-12-28Bibliographically approved
Abghari, S., Boeva, V., Casalicchio, E. & Exner, P. (2022). An Inductive System Monitoring Approach for GNSS Activation. In: Maglogiannis, I, Iliadis, L, Macintyre, J, Cortez, P (Ed.), IFIP Advances in Information and Communication Technology: . Paper presented at 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, Hersonissos, 17 June 2022 - 20 June 2022 (pp. 437-449). Springer Science+Business Media B.V., 647
Open this publication in new window or tab >>An Inductive System Monitoring Approach for GNSS Activation
2022 (English)In: 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, p. 437-449Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868422X ; 647
Keywords
Activation analysis, Chemical activation, Global positioning system, Long Term Evolution (LTE), Monitoring, Radio navigation, Clustering analysis, Context detection, Environmental context detection, Environmental contexts, Global navigation satellite system signal, Global Navigation Satellite Systems, Inductive system, Inductive system monitoring, System monitoring, System signals, Base stations, GNSS signal
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-23550 (URN)10.1007/978-3-031-08337-2_36 (DOI)000927893200036 ()2-s2.0-85133294290 (Scopus ID)9783031083365 (ISBN)
Conference
18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, Hersonissos, 17 June 2022 - 20 June 2022
Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2023-03-09Bibliographically approved
Ahmadi Mehri, V., Arlos, P. & Casalicchio, E. (2022). Automated Context-Aware Vulnerability Risk Management for Patch Prioritization. Electronics, 11(21), Article ID 3580.
Open this publication in new window or tab >>Automated Context-Aware Vulnerability Risk Management for Patch Prioritization
2022 (English)In: Electronics, E-ISSN 2079-9292, Vol. 11, no 21, article id 3580Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
patch prioritization, risk management, security management, vulnerability management
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-23982 (URN)10.3390/electronics11213580 (DOI)000883429300001 ()2-s2.0-85141721682 (Scopus ID)
Note

open access

Available from: 2022-11-24 Created: 2022-11-24 Last updated: 2023-04-26Bibliographically approved
Al-Saedi, A. A., Boeva, V., Casalicchio, E. & Exner, P. (2022). Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview. Sensors, 22(15), Article ID 5544.
Open this publication in new window or tab >>Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview
2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 15, article id 5544Article, review/survey (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
artificial intelligence, context-awareness, edge computing, wireless sensor network, computer network, human, wireless communication, Computer Communication Networks, Humans, Wireless Technology
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-23537 (URN)10.3390/s22155544 (DOI)000839768900001 ()2-s2.0-85135202158 (Scopus ID)
Note

open access

Available from: 2022-08-12 Created: 2022-08-12 Last updated: 2022-08-26Bibliographically approved
Al-Saedi, A. A., Boeva, V. & Casalicchio, E. (2022). FedCO: Communication-Efficient Federated Learning via Clustering Optimization †. Future Internet, 14(12), Article ID 377.
Open this publication in new window or tab >>FedCO: Communication-Efficient Federated Learning via Clustering Optimization †
2022 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 14, no 12, article id 377Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
clustering, communication efficiency, convolutional neural network, federated learning, Internet of Things, Convolutional neural networks, Cost reduction, Learning systems, Privacy-preserving techniques, Central servers, Clustering optimizations, Clusterings, Communication cost, Optimization approach, Shared model, Workers'
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24176 (URN)10.3390/fi14120377 (DOI)000901037100001 ()2-s2.0-85144590253 (Scopus ID)
Note

open access

Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-08-03Bibliographically approved
Al-Saedi, A. A., Casalicchio, E. & Boeva, V. (2021). An Energy-aware Multi-Criteria Federated Learning Model for Edge Computing. In: Younas M., Awan I., Unal P. (Ed.), Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021: . Paper presented at 8th International Conference on Future Internet of Things and Cloud, FiCloud 2021, Virtual, Online, 23 August 2021 through 25 August 2021 (pp. 134-143). IEEE
Open this publication in new window or tab >>An Energy-aware Multi-Criteria Federated Learning Model for Edge Computing
2021 (English)In: Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021 / [ed] Younas M., Awan I., Unal P., IEEE, 2021, p. 134-143Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Federated Learning, Clustering Analysis, Energy consumption, battery lifetime, Human Activity Recognition
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-22236 (URN)10.1109/FiCloud49777.2021.00027 (DOI)2-s2.0-85119667934 (Scopus ID)9781665425742 (ISBN)
Conference
8th International Conference on Future Internet of Things and Cloud, FiCloud 2021, Virtual, Online, 23 August 2021 through 25 August 2021
Available from: 2021-10-26 Created: 2021-10-26 Last updated: 2021-12-06Bibliographically approved
Casalicchio, E. & Gualandi, G. (2021). ASiMOV: A self-protecting control application for the smart factory. Future generations computer systems, 115, 213-235
Open this publication in new window or tab >>ASiMOV: A self-protecting control application for the smart factory
2021 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 115, p. 213-235Article in journal (Refereed) Published
Abstract [en]

The evolution of manufacturing systems into a smart factory brings advantages but also increased cyber-risks. This paper investigates the problem of intrusion detection and autonomous response to cyber-attacks targeting the control logic of industrial control applications for the smart factory. Specifically, we propose ASiMOV (Asynchronous Modular Verification), a self-protecting architecture for cyber–physical systems realizing a verifiable control application. ASiMOV is inspired by modular redundancy and leverages virtualization technologies to respond and to prevent cyber-attacks to the control logic. Using simulation experiments, we evaluate: the effects of an attack on an industrial control application enhanced by ASiMOV; the delay introduced by ASiMOV within a control loop; and the cyber-attack detection delay. Results show that, in the simulated scenario, the controller can work with a sampling rate of up to 200 Hertz. Any tampering with the control logic is detected without false positives/negatives in a time equal to the latency between the proposed control application and the proposed IDS (e.g., tens to hundreds of milliseconds). © 2020

Place, publisher, year, edition, pages
Elsevier B.V., 2021
Keywords
Cyber-security, Cyber–physical systems, Event-based control, Industrial Control Systems, Microservices, Self-protection, Virtualization, Computer circuits, Control systems, Crime, Intrusion detection, Manufacture, Network security, Silicon, Autonomous response, Control applications, Industrial control applications, Modular redundancy, Modular verification, Physical systems, Self protecting, Virtualization technologies, Computer crime
National Category
Control Engineering
Identifiers
urn:nbn:se:bth-20471 (URN)10.1016/j.future.2020.09.003 (DOI)000591438600009 ()
Available from: 2020-09-25 Created: 2020-09-25 Last updated: 2021-01-07Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2021). Energy-Aware Very Fast Decision Tree. International Journal of Data Science and Analytics, 11(2), 105-126
Open this publication in new window or tab >>Energy-Aware Very Fast Decision Tree
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2021 (English)In: International Journal of Data Science and Analytics, ISSN 2364-415X, Vol. 11, no 2, p. 105-126Article in journal (Refereed) Published
Abstract [en]

Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2021
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-19150 (URN)10.1007/s41060-021-00246-4 (DOI)000631559600001 ()2-s2.0-85102938796 (Scopus ID)
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2021-07-30Bibliographically approved
Ahmadi Mehri, V., Arlos, P. & Casalicchio, E. (2021). Normalization Framework for Vulnerability Risk Management in Cloud. In: Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021: . Paper presented at 8th International Conference on Future Internet of Things and Cloud, FiCloud 2021, Virtual, Online, 23 August through 25 August 2021 (pp. 99-106). IEEE
Open this publication in new window or tab >>Normalization Framework for Vulnerability Risk Management in Cloud
2021 (English)In: Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021, IEEE, 2021, p. 99-106Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Risk Assessment, Vulnerability, Cloud security
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-22100 (URN)10.1109/FiCloud49777.2021.00022 (DOI)2-s2.0-85115338714 (Scopus ID)
Conference
8th International Conference on Future Internet of Things and Cloud, FiCloud 2021, Virtual, Online, 23 August through 25 August 2021
Available from: 2021-09-02 Created: 2021-09-02 Last updated: 2023-06-07Bibliographically approved
Al-Saedi, A. A., Boeva, V. & Casalicchio, E. (2021). Reducing Communication Overhead of Federated Learning through Clustering Analysis. In: 26th IEEE Symposium on Computers and Communications (ISCC 2021): . Paper presented at 26th IEEE Symposium on Computers and Communications, ISCC 2021, Athens, 5 September 2021 through 8 September 2021. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Reducing Communication Overhead of Federated Learning through Clustering Analysis
2021 (English)In: 26th IEEE Symposium on Computers and Communications (ISCC 2021), Institute of Electrical and Electronics Engineers (IEEE), 2021Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Series
Proceedings of the IEEE Symposium on Computers and Communications, ISSN 15301346
Keywords
Federated Learning, Communication Costs, Clustering Analysis, Human Activity Recognition
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-22237 (URN)10.1109/ISCC53001.2021.9631391 (DOI)000936276000023 ()2-s2.0-85123206373 (Scopus ID)9781665427449 (ISBN)
Conference
26th IEEE Symposium on Computers and Communications, ISCC 2021, Athens, 5 September 2021 through 8 September 2021
Available from: 2021-10-26 Created: 2021-10-26 Last updated: 2024-01-01Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-3118-5058

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