Change search
Link to record
Permanent link

Direct link
Lundberg, Lars
Publications (10 of 175) Show all publications
Ilie, D., Grahn, H., Lundberg, L., Westerhagen, A., Granbom, B. & Höök, A. (2023). Avoiding Detection by Hostile Nodes in Airborne Tactical Networks. Future Internet, 15(6), Article ID 204.
Open this publication in new window or tab >>Avoiding Detection by Hostile Nodes in Airborne Tactical Networks
Show others...
2023 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 15, no 6, article id 204Article in journal (Refereed) Published
Abstract [en]

Contemporary airborne radio networks are usually implemented using omnidirectional antennas. Unfortunately, such networks suffer from disadvantages such as easy detection by hostile aircraft and potential information leakage. In this paper, we present a novel mobile ad hoc network (MANET) routing protocol based on directional antennas and situation awareness data that utilizes adaptive multihop routing to avoid sending information in directions where hostile nodes are present. Our protocol is implemented in the OMNEST simulator and evaluated using two realistic flight scenarios involving 8 and 24 aircraft, respectively. The results show that our protocol has significantly fewer leaked packets than comparative protocols, but at a slightly higher cost in terms of longer packet lifetime.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
mobile ad hoc networks, routing, protocol
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25214 (URN)10.3390/fi15060204 (DOI)001017172700001 ()2-s2.0-85163779771 (Scopus ID)
Projects
NFFP7 (Call 2)-Riktad luftdatalänk
Funder
Vinnova
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-07Bibliographically approved
Lundberg, L. (2023). Bibliometric mining of research directions and trends for big data. Journal of Big Data, 10(1), Article ID 112.
Open this publication in new window or tab >>Bibliometric mining of research directions and trends for big data
2023 (English)In: Journal of Big Data, E-ISSN 2196-1115, Vol. 10, no 1, article id 112Article in journal (Refereed) Published
Abstract [en]

In this paper a program and methodology for bibliometric mining of research trends and directions is presented. The method is applied to the research area Big Data for the time period 2012 to 2022, using the Scopus database. It turns out that the 10 most important research directions in Big Data are Machine learning, Deep learning and neural networks, Internet of things, Data mining, Cloud computing, Artificial intelligence, Healthcare, Security and privacy, Review, and Manufacturing. The role of Big Data research in different fields of science and technology is also analysed. For four geographic regions (North America, European Union, China, and The Rest of the World) different activity levels in Big Data during different parts of the time period are analysed. North America was the most active region during the first part of the time period. During the last years China is the most active region. The citation scores for documents from different regions and from different research directions within Big Data are also compared. North America has the highest average citation score among the geographic regions and the research direction Review has the highest average citation score among the research directions. The program and methodology for bibliometric mining developed in this study can be used also for other large research areas. Now that the program and methodology have been developed, it is expected that one could perform a similar study in some other research area in a couple of days. © 2023, The Author(s).

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023
Keywords
Bibliometrics, Fields of science and technology, Geographic regions, Research directions, Research trends, Scopus database
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-25217 (URN)10.1186/s40537-023-00793-6 (DOI)001022426100002 ()2-s2.0-85163820321 (Scopus ID)
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-14Bibliographically approved
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
Show others...
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
Ilie, D., Grahn, H., Lundberg, L. & Westerhagen, A. (2023). Topology Control for Directed DataLinks between Airborne Platforms: Directed Air Data Link: WP3 report. Karlskrona: Blekinge Tekniska Högskola
Open this publication in new window or tab >>Topology Control for Directed DataLinks between Airborne Platforms: Directed Air Data Link: WP3 report
2023 (English)Report (Other academic)
Abstract [en]

Contemporary airborne radio networks are usually implemented using omnidirectional antennas. Unfortunately, such networks suffer from disadvantages such as easy detection by hostile aircraft and potential information leakage. In addition, tactical links used for military communication rely on NATO-specific standards such as Link 16, which are becoming outdated. 

To this end we are investigating the feasibility of replacing omnidirectional communication with directed communication, which will address the disadvantages mentioned above. In addition, we definine a communication architecture based on the conventional Ethernet and TCP/IP protocol stack, which will ease management and interoperability with existing Internet-based system 

In this report, we briefly review the TCP/IP stack and the services offerd at each layer of the stack. Furthermore, we review existing litterature involving mobile ad hoc network (MANET) protocols used for airborne networks along with various performance studies in the same area. Finally, we propose a novel MANET routing protocol based on directional antennas and situation awareness data that utilizes adaptive multihop routing to avoid sending information in directions where hostile nodes are present. 

Our protocol is implemented in the OMNEST simulator and evaluated using two realistic flight scenarios involving 8 and 24 aircraft, respectively. The results show that our protocol has significantly fewer leaked packets than comparative protocols, but at a slightly higher cost in terms of longer packet lifetime. 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2023
Keywords
routing, MANET, directed communication, FANET
National Category
Communication Systems
Research subject
Computer Science; Telecommunication Systems
Identifiers
urn:nbn:se:bth-25288 (URN)
Projects
Directed Air Data Link (Vinnova)
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-08-22Bibliographically approved
Dasari, S. K., Cheddad, A., Palmquist, J. & Lundberg, L. (2022). Clustering-based Adaptive Data Augmentation for Class-imbalance in Machine Learning (CADA): Additive Manufacturing Use-case. Neural Computing & Applications
Open this publication in new window or tab >>Clustering-based Adaptive Data Augmentation for Class-imbalance in Machine Learning (CADA): Additive Manufacturing Use-case
2022 (English)In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed) Epub ahead of print
Abstract [en]

Large amount of data are generated from in-situ monitoring of additive manufacturing (AM) processes which is later used in prediction modelling for defect classification to speed up quality inspection of products. A high volume of this process data is defect-free (majority class) and a lower volume of this data has defects (minority class) which result in the class-imbalance issue. Using imbalanced datasets, classifiers often provide sub-optimal classification results i.e. better performance on the majority class than the minority class. However, it is important for process engineers that models classify defects more accurately than the class with no defects since this is crucial for quality inspection. Hence, we address the class-imbalance issue in manufacturing process data to support in-situ quality control of additive manufactured components.  For this, we propose cluster-based adaptive data augmentation (CADA) for oversampling to address the class-imbalance problem. Quantitative experiments are conducted to evaluate the performance of the proposed method and to compare with other selected oversampling methods using AM datasets from an aerospace industry and a publicly available casting manufacturing dataset. The results show that CADA outperformed random oversampling and the SMOTE method and is similar to random data augmentation and cluster-based oversampling. Furthermore, the results of the statistical significance test show that there is a significant difference between the studied methods.  As such, the CADA method can be considered as an alternative method for oversampling to improve the performance of models on the minority class. 

Place, publisher, year, edition, pages
Springer London, 2022
Keywords
Class-imbalance, Melt-pool defects classification, Aerospace application, Additive Manufacturing, Polar Transformation, Random Forests
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-22028 (URN)10.1007/s00521-022-07347-6 (DOI)000800995800001 ()
Note

open access

Available from: 2021-08-20 Created: 2021-08-20 Last updated: 2022-06-10Bibliographically approved
Anwar, M., Lundberg, L. & Borg, A. (2022). Improving anomaly detection in SCADA network communication with attribute extension. Energy Informatics, 5(1), Article ID 69.
Open this publication in new window or tab >>Improving anomaly detection in SCADA network communication with attribute extension
2022 (English)In: Energy Informatics, E-ISSN 2520-8942, Vol. 5, no 1, article id 69Article in journal (Refereed) Published
Abstract [en]

Network anomaly detection for critical infrastructure supervisory control and data acquisition (SCADA) systems is the first line of defense against cyber-attacks. Often hybrid methods, such as machine learning with signature-based intrusion detection methods, are employed to improve the detection results. Here an attempt is made to enhance the support vector-based outlier detection method by leveraging behavioural attribute extension of the network nodes. The network nodes are modeled as graph vertices to construct related attributes that enhance network characterisation and potentially improve unsupervised anomaly detection ability for SCADA network. IEC 104 SCADA protocol communication data with good domain fidelity is utilised for empirical testing. The results demonstrate that the proposed approach achieves significant improvements over the baseline approach (average F1F1 score increased from 0.6 to 0.9, and Matthews correlation coefficient (MCC) from 0.3 to 0.8). The achieved outcome also surpasses the unsupervised scores of related literature. For critical networks, the identification of attacks is indispensable. The result shows an insignificant missed-alert rate (0.3%0.3% on average), the lowest among related works. The gathered results show that the proposed approach can expose rouge SCADA nodes reasonably and assist in further pruning the identified unusual instances.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Supervisory control and data acquisition, Network intrusion detection, Machine learning, IEC 60870-5-104, Attribute extension
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-24125 (URN)10.1186/s42162-022-00252-1 (DOI)2-s2.0-85144520103 (Scopus ID)
Note

Open access

Available from: 2022-12-21 Created: 2022-12-21 Last updated: 2023-01-10Bibliographically approved
Lundberg, L. & Grahn, H. (2022). Research Trends, Enabling Technologies and Application Areas for Big Data. Algorithms, 15(8), Article ID 280.
Open this publication in new window or tab >>Research Trends, Enabling Technologies and Application Areas for Big Data
2022 (English)In: Algorithms, E-ISSN 1999-4893, Vol. 15, no 8, article id 280Article in journal (Refereed) Published
Abstract [en]

The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six groups of important enabling technologies for Big Data applications and systems. Then, using bibliometrics and an extensive literature review of more than 80 papers, we identify the most important research trends in these areas. In addition, our bibliometric analysis also includes trends in different geographical regions. Our results indicate that manufacturing and agriculture or forestry are the two application areas with the fastest growth. Furthermore, our bibliometric study shows that deep learning and edge or fog computing are the enabling technologies increasing the most. We believe that the data presented in this paper provide a good overview of the current research trends in Big Data and that this kind of information is very useful when setting strategic agendas for Big Data research.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
survey, Big Data, telecommunication, image processing, smart cities, manufacturing, parallel processing, storage systems, cloud computing, deep learning
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-23617 (URN)10.3390/a15080280 (DOI)000846415100001 ()2-s2.0-85137271059 (Scopus ID)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

open access

Available from: 2022-09-12 Created: 2022-09-12 Last updated: 2023-03-29Bibliographically approved
Singh, S. P., Ali, N. b. & Lundberg, L. (2022). Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation. Sensors, 22(8), Article ID 3017.
Open this publication in new window or tab >>Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation
2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 8, article id 3017Article in journal (Refereed) Published
Abstract [en]

Advances in 5G and the Internet of Things (IoT) have to cater to the diverse and varying needs of different stakeholders, devices, sensors, applications, networks, and access technologies that come together for a dedicated IoT network for a synergistic purpose. Therefore, there is a need for a solution that can assimilate the various requirements and policies to dynamically and intelligently orchestrate them in the dedicated IoT network. Thus we identify and describe a representative industry-relevant use case for such a smart and adaptive environment through interviews with experts from a leading telecommunication vendor. We further propose and evaluate candidate architectures to achieve dynamic and intelligent orchestration in such a smart environment using a systematic approach for architecture design and by engaging six senior domain and IoT experts. The candidate architecture with an adaptive and intelligent element (“Smart AAA agent”) was found superior for modifiability, scalability, and performance in the assessments. This architecture also explores the enhanced role of authentication, authorization, and accounting (AAA) and makes the base for complete orchestration. The results indicate that the proposed architecture can meet the requirements for a dedicated IoT network, which may be used in further research or as a reference for industry solutions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
5G, accounting, authentication, and authorization (AAA), architecture assessment, artificial intelligence, Internet of Things (IoT), multi-access edge computing (MEC), online gaming, smart and adaptive environment, 5G mobile communication systems, Authentication, Authorization, Network architecture, Accounting, authentication, and authorization, Authentication and authorization, Edge computing, Internet of thing, Multi-access edge computing, Multiaccess, On-line gaming, Smart architectures, Internet of things
National Category
Media and Communication Technology Communication Systems
Identifiers
urn:nbn:se:bth-22882 (URN)10.3390/s22083017 (DOI)000785325300001 ()2-s2.0-85128214506 (Scopus ID)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsKnowledge Foundation, 20170213Knowledge Foundation, 20180127
Note

open access

Available from: 2022-04-29 Created: 2022-04-29 Last updated: 2023-08-17Bibliographically approved
Anwar, M., Borg, A. & Lundberg, L. (2021). A comparison of Unsupervised Learning Algorithms for Intrusion Detection in IEC 104 SCADA Protocol. In: Proceedings - International Conference on Machine Learning and Cybernetics: . Paper presented at 20th International Conference on Machine Learning and Cybernetics, ICMLC 2021, Adelaide, Australia, 4 December 2021 through 5 December 2021. IEEE Computer Society
Open this publication in new window or tab >>A comparison of Unsupervised Learning Algorithms for Intrusion Detection in IEC 104 SCADA Protocol
2021 (English)In: Proceedings - International Conference on Machine Learning and Cybernetics, IEEE Computer Society , 2021Conference paper, Published paper (Refereed)
Abstract [en]

The power grid is a build-up of a mesh of thousands of sensors, embedded devices, and terminal units that communicate over different media. The heterogeneity of modern and legacy equipment calls for attention towards diverse network security measures. The critical infrastructure employs different security measures to detect and prevent adversaries, e.g., through signature-based tools. These approaches lack the potential to identify unknown attacks. Machine learning has the prospective to address novel attack vectors. This paper systematically evaluates the efficacy of learning algorithms from different families for intrusion detection in IEC 60870-5-104 protocol. One-class SVM and k-Nearest Neighbour unsupervised learning models show small potential when being tested on the IEC 104 unseen dataset with Area Under the Curve score 0.64 and 0.59, in the same order; and Matthews Correlation Coefficient value 0.3 and 0.2, respectively. The experimental results suggest little feasibility of the evaluated unsupervised learning approaches for anomaly detection in IEC 104 communication and recommend coupling it with other anomaly detection techniques. © 2021 IEEE.

Place, publisher, year, edition, pages
IEEE Computer Society, 2021
Series
PROCEEDINGS OF 2021 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), ISSN 2160-133X
Keywords
Friedman Test, IEC 60870-5-104, Intrusion Detection, SCADA protocol, Unsupervised Machine Learning, Anomaly detection, Electric power transmission networks, Learning algorithms, Nearest neighbor search, Network security, Support vector machines, Unsupervised learning, Embedded device, Intrusion-Detection, Power grids, SCADA Protocols, Security measure, Unsupervised learning algorithms
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:bth-22856 (URN)10.1109/ICMLC54886.2021.9737267 (DOI)000805238500010 ()2-s2.0-85127783347 (Scopus ID)9781665466080 (ISBN)
Conference
20th International Conference on Machine Learning and Cybernetics, ICMLC 2021, Adelaide, Australia, 4 December 2021 through 5 December 2021
Available from: 2022-04-22 Created: 2022-04-22 Last updated: 2022-07-01Bibliographically approved
Dasari, S. K., Cheddad, A., Lundberg, L. & Palmquist, J. (2021). Active Learning to Support In-situ Process Monitoring in Additive Manufacturing. In: Wani M.A., Sethi I.K., Shi W., Qu G., Raicu D.S., Jin R. (Ed.), Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021: . Paper presented at 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Virtual, Online, 13 December 2021 through 16 December 2021 (pp. 1168-1173). IEEE
Open this publication in new window or tab >>Active Learning to Support In-situ Process Monitoring in Additive Manufacturing
2021 (English)In: Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 / [ed] Wani M.A., Sethi I.K., Shi W., Qu G., Raicu D.S., Jin R., IEEE, 2021, p. 1168-1173Conference paper, Published paper (Refereed)
Abstract [en]

This paper aims to address data labelling issues in process data to support in-situ process monitoring of additive manufactured components. For this, we adopted an active learning (AL) approach to minimise the manual effort for data labelling for classification models. In this study, we present an approach that utilises pre-trained models to extract deep features from images, and clustering and query by committee sampling to select the representative samples to build defect classification models. We conduct quantitative experiments to evaluate the proposed method's performance and compare it with other selected state-of-the-art AL approaches using a dataset of additive manufacturing (AM) and a publicly available dataset. The experimental results show that the proposed approach outperforms AL with committee based sampling, and AL with clustering and random sampling. The results of the statistical significance test show that there is a significant difference between the studied AL approaches. Hence, the proposed AL approach can be considered an alternative method to reduce labelling costs when building defects classification models, whose generalizability is most likely plausible.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Data labelling, Defects classification, Aerospace application, Random forests, Support vector machines.
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-22029 (URN)10.1109/ICMLA52953.2021.00190 (DOI)000779208200182 ()2-s2.0-85125852650 (Scopus ID)9781665443371 (ISBN)
Conference
20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Virtual, Online, 13 December 2021 through 16 December 2021
Available from: 2021-08-20 Created: 2021-08-20 Last updated: 2022-05-30Bibliographically approved
Projects
AGILESEC – Agile development of security critical software [20150214]; Blekinge Institute of Technology; Publications
Vishnubhotla, S. D., Mendes, E. & Lundberg, L. (2021). Understanding the Perceived Relevance of Capability Measures: A Survey of Agile Software Development Practitioners. Journal of Systems and Software, 180, Article ID 111013.
Organisations

Search in DiVA

Show all publications