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Publications (5 of 5) Show all publications
Sprei, F., Kazemzadeh, K., Faxer, A., Einarson Lindvall, E., Lundahl, J., Rosell, J., . . . Engdahl, H. (2023). How can e-scooter better contribute to a sustainable transport system?.
Open this publication in new window or tab >>How can e-scooter better contribute to a sustainable transport system?
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2023 (English)Other (Other academic)
Abstract [en]

The eSPARK project examines the sustainability profile of the popular shared e-scooters through policy analysis, usage data analysis, surveys, and life cycle assessment. Policies and attempts to regulate e-scooters in Swedish and European cities are studied and discussed with stakeholders. The LCA-results suggest that factors such as how e-scooters are collected and distributed, and the total ridden kilometers have significant impact on their environmental impact. The project also suggests different methods that can support cities to predict the geographical area of the e-scooters and offers insights about how e-scooters are used in the cities. Usage data and the surveys show that they are used by active people in areas with a lot of activities, especially restaurants and clubs. Users are likely to have a driving license, to frequently use a car but also to have a monthly pass for public transport. Thus, escooters have a potential to mitigate congestion on roads and public transport but may lead to more traffic on bike infrastructure instead.

National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:ri:diva-67528 (URN)
Note

This project is granted by the Swedish Energy Agency (Dnr 2020-011467)

Available from: 2023-10-12 Created: 2023-10-12 Last updated: 2024-02-28Bibliographically approved
Mowla, N. I., Rosell, J. & Vahidi, A. (2022). Dynamic Voting based Explainable Intrusion Detection System for In-vehicle Network. In: International Conference on Advanced Communication Technology, ICACT: . Paper presented at 24th International Conference on Advanced Communication Technology, ICACT 2022, 13 February 2022 through 16 February 2022 (pp. 406-411). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Dynamic Voting based Explainable Intrusion Detection System for In-vehicle Network
2022 (English)In: International Conference on Advanced Communication Technology, ICACT, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 406-411Conference paper, Published paper (Refereed)
Abstract [en]

A modern vehicle contains a large number of electronic components communicating over a large in-vehicle network. While the operation of this network is crucial, some implementations are vulnerable to a number of security attacks while lacking sufficient security measures. Intrusion detection systems have been proposed as a possible solution to this, with those using machine learning receiving much attention. However, such systems may be hard to interpret and understand. In this work, we propose an automotive intrusion detection system that utilizes Random Forest with a dynamic voting technique to provide a robust solution with interpretability through feature and model exploration. The proposed solution is evaluated using two publicly available datasets and demonstrates stable performance when compared to similar solutions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
ensemble learning, explainable AI, In-vehicle network, intrusion detection, random forest, Computer crime, Decision trees, Vehicles, Automotives, Electronic component, In-vehicle networks, Intrusion Detection Systems, Intrusion-Detection, Random forests, Security attacks, Security measure
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-59771 (URN)10.23919/ICACT53585.2022.9728968 (DOI)2-s2.0-85127519007 (Scopus ID)9791188428090 (ISBN)
Conference
24th International Conference on Advanced Communication Technology, ICACT 2022, 13 February 2022 through 16 February 2022
Available from: 2022-07-04 Created: 2022-07-04 Last updated: 2023-05-22Bibliographically approved
Mowla, N., Rosell, J. & Vahidi, A. (2022). Dynamic Voting based Explainable Intrusion Detection System for In-vehicle Network. In: 2022 24th International Conference on Advanced Communication Technology (ICACT) 13-16 Feb. 2022: . Paper presented at 2022 24th International Conference on Advanced Communication Technology (ICACT) 13-16 Feb. 2022 (pp. 406). (24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity)
Open this publication in new window or tab >>Dynamic Voting based Explainable Intrusion Detection System for In-vehicle Network
2022 (English)In: 2022 24th International Conference on Advanced Communication Technology (ICACT) 13-16 Feb. 2022, 2022, no 24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity, p. 406-Conference paper, Published paper (Other academic)
Abstract [en]

A modern vehicle contains a large number of electronic components communicating over a large in-vehicle network. While the operation of this network is crucial, some implementations are vulnerable to a number of security attacks while lacking sufficient security measures. Intrusion detection systems have been proposed as a possible solution to this, with those using machine learning receiving much attention. However, such systems may be hard to interpret and understand. In this work, we propose an automotive intrusion detection system that utilizes Random Forest with a dynamic voting technique to provide a robust solution with interpretability through feature and model exploration. The proposed solution is evaluated using two publicly available datasets and demonstrates stable performance when compared to similar solutions.

National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-62471 (URN)10.23919/ICACT53585.2022 (DOI)979-11-88428-09-0 (ISBN)
Conference
2022 24th International Conference on Advanced Communication Technology (ICACT) 13-16 Feb. 2022
Available from: 2023-01-24 Created: 2023-01-24 Last updated: 2023-05-22Bibliographically approved
Rosell, J., Englund, C., Vahidi, A., Mowla, N., Magazinius, A. & Järpe, E. (2021). A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection. In: : . Paper presented at Fast Zero´21, Society of Automotive Engineers of Japan, 2021. JSAE
Open this publication in new window or tab >>A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection
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2021 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Modern vehicles have numerous electronic control units (ECUs) that constantly communicate over embedded in-vehicle networks (IVNs) comprised of controlled area network (CAN) segments. The simplicity and size-constrained 8-byte payload of the CAN bus technology makes it infeasible to integrate authenticity and integrity-based protection mechanisms. Thus, a malicious component will be able to inject malicious data into the network with minimal risk for detection. Such vulnerabilities have been demonstrated with various security attacks such as the flooding, fuzzing, and malfunction attacks. A practical approach to improve security in modern vehicles is to monitor the CAN bus traffic to detect anomalies. However, to administer such an intrusion detection system (IDS) with a general approach faces some challenges. First, the proprietary encodings of the CAN data fields need to be omitted as they are intellectual property of the original equipment manufacturers (OEMs) and differ across vehicle manufacturers and their models. Secondly, such general and practical IDS approach must also be computationally efficient in terms of speed and accuracy. Traditional IDSs for computer networks generally utilize a rule or signature-based approach. More recently, the approach of using machine learning (ML) with efficient feature representation has shown significant success because of faster detection and lower development and maintenance costs. Therefore, an efficient data aggregation technique with enhanced frequency-based feature representation to improve the performance of MLbased IDS for the IVNs is proposed. The performance gain was verified with the Survival Analysis Dataset for automobile IDS.

Place, publisher, year, edition, pages
JSAE, 2021
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-58989 (URN)
Conference
Fast Zero´21, Society of Automotive Engineers of Japan, 2021
Available from: 2022-04-12 Created: 2022-04-12 Last updated: 2023-05-22Bibliographically approved
Rosell, J., Englund, C., Vahidi, A., Mowla, N. I., Magazinius, A. & Järpe, E. (2021). A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection. In: FAST-zero '21: . Paper presented at Fast Zero´21, Society of Automotive Engineers of Japan, 2021. Japan: Society of Automotive Engineers
Open this publication in new window or tab >>A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection
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2021 (English)In: FAST-zero '21, Japan: Society of Automotive Engineers, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Modern vehicles have numerous electronic control units (ECUs) that constantly communicate over embedded in-vehicle networks (IVNs) comprised of controlled area network (CAN) segments. The simplicity and size-constrained 8-byte payload of the CAN bus technology makes it infeasible to integrate authenticity and integrity-based protection mechanisms. Thus, a malicious component will be able to inject malicious data into the network with minimal risk for detection. Such vulnerabilities have been demonstrated with various security attacks such as the flooding, fuzzing, and malfunction attacks. A practical approach to improve security in modern vehicles is to monitor the CAN bus traffic to detect anomalies. However, to administer such an intrusion detection system (IDS) with a general approach faces some challenges. First, the proprietary encodings of the CAN data fields need to be omitted as they are intellectual property of the original equipment manufacturers (OEMs) and differ across vehicle manufacturers and their models. Secondly, such general and practical IDS approach must also be computationally efficient in terms of speed and accuracy. Traditional IDSs for computer networks generally utilize a rule or signature-based approach. More recently, the approach of using machine learning (ML) with efficient feature representation has shown significant success because of faster detection and lower development and maintenance costs. Therefore, an efficient data aggregation technique with enhanced frequency-based feature representation to improve the performance of MLbased IDS for the IVNs is proposed. The performance gain was verified with the Survival Analysis Dataset for automobile IDS.

Place, publisher, year, edition, pages
Japan: Society of Automotive Engineers, 2021
Keywords
Data mining, in-vehicle network, intrusion detection, random forest, feature selection.
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-63967 (URN)
Conference
Fast Zero´21, Society of Automotive Engineers of Japan, 2021
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-05-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8511-6867

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