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  • 1.
    Mowla, Nishat I.
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Rosell, Joakim
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Vahidi, Arash
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Dynamic Voting based Explainable Intrusion Detection System for In-vehicle Network2022In: International Conference on Advanced Communication Technology, ICACT, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 406-411Conference 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.

  • 2.
    Mowla, Nishat
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Rosell, Joakim
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Vahidi, Arash
    RISE Research Institutes of Sweden.
    Dynamic Voting based Explainable Intrusion Detection System for In-vehicle Network2022In: 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 (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.

  • 3.
    Rosell, Joakim
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Englund, Cristofer
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems. Halmstad University, Sweden.
    Vahidi, Arash
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Mowla, Nishat I
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Magazinius, Ana
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Järpe, Eric
    Halmstad University, Sweden.
    A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection2021In: FAST-zero '21, Japan: Society of Automotive Engineers, 2021Conference 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.

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  • 4.
    Rosell, Joakim
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Englund, Cristofer
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems. Halmstad University, Sweden.
    Vahidi, Arash
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Mowla, Nishat
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Magazinius, Ana
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Järpe, Eric
    Halmstad University, Sweden.
    A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection2021Conference 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.

  • 5.
    Sprei, Frances
    et al.
    Chalmers University of Technology, Sweden.
    Kazemzadeh, Khashayar
    Chalmers University of Technology, Sweden.
    Faxer, Anne
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Einarson Lindvall, Elin
    RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.
    Lundahl, Jenny
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Rosell, Joakim
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Melnyk, Kateryna
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Holmgren, Kristina
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Habibi, Shiva
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Stenberg, Susanne
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Pettersson, Stefan
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Wedlin, Johan
    Tånggudden, Sweden.
    Engdahl, Henrik
    Nimling, Sweden.
    How can e-scooter better contribute to a sustainable transport system?2023Other (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.

    Download full text (pdf)
    fulltext
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  • ieee
  • modern-language-association-8th-edition
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