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An artificial neural network based anomaly detection method in CAN bus messages in vehicles

Conference Paper


Abstract


  • Controller Area Network is the bus standard that works as a central system inside the vehicles for communicating in-vehicle messages. Despite having many advantages, attackers may hack into a car system through CAN bus, take control of it and cause serious damage. For, CAN bus lacks security services like authentication, encryption etc. Therefore, an anomaly detection system must be integrated with CAN bus in vehicles. In this paper, we proposed an Artificial Neural Network based anomaly detection method to identify illicit messages in CAN bus. We trained our model with two types of attacks so that it can efficiently identify the attacks. When tested, the proposed algorithm showed high performance in detecting Denial of Service attacks (with accuracy 100%) and Fuzzy attacks (with accuracy 99.98%).

Publication Date


  • 2021

Citation


  • Paul, A., & Islam, M. R. (2021). An artificial neural network based anomaly detection method in CAN bus messages in vehicles. In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0, ACMI 2021. doi:10.1109/ACMI53878.2021.9528201

Scopus Eid


  • 2-s2.0-85115707286

Web Of Science Accession Number


Abstract


  • Controller Area Network is the bus standard that works as a central system inside the vehicles for communicating in-vehicle messages. Despite having many advantages, attackers may hack into a car system through CAN bus, take control of it and cause serious damage. For, CAN bus lacks security services like authentication, encryption etc. Therefore, an anomaly detection system must be integrated with CAN bus in vehicles. In this paper, we proposed an Artificial Neural Network based anomaly detection method to identify illicit messages in CAN bus. We trained our model with two types of attacks so that it can efficiently identify the attacks. When tested, the proposed algorithm showed high performance in detecting Denial of Service attacks (with accuracy 100%) and Fuzzy attacks (with accuracy 99.98%).

Publication Date


  • 2021

Citation


  • Paul, A., & Islam, M. R. (2021). An artificial neural network based anomaly detection method in CAN bus messages in vehicles. In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0, ACMI 2021. doi:10.1109/ACMI53878.2021.9528201

Scopus Eid


  • 2-s2.0-85115707286

Web Of Science Accession Number