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Title
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Electric Vehicles CAN Bus Cyber Attacks Detection Using Adaptive Neuro Fuzzy Inference System
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Type
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Refereeing
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Keywords
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Adaptive Neuro Fuzzy Inference System (ANFIS), Cyber-Attacks Detection, EV CAN Bus, Fuzzy C-Means clustering (FCM)
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Abstract
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The Electric Vehicle (EV) industry has recently experienced notable technological progress in the field of Controller Area Network (CAN) protocol. Yet, the use of CAN bus protocol in EVs is exposed to intrinsic cybersecurity risks and consequently causing EV damages as a result of lack of authentication, authorization, and accounting mechanisms. This paper examines the vulnerabilities within the EVs’ CAN bus protocol and explores potential strategies for mitigating cybersecurity threats (i.e. Denial of Service (DOS) and impersonation attacks). In particular, the paper proposes Adaptive Neuro Fuzzy Inference System (ANFIS) based detection techniques superimposed with Subtractive Clustering (SC) and Fuzzy C-Means clustering (FCM). Results demonstrate that the proposed ANFIS-SC and ANFIS-FCM detection techniques had higher testing accuracies and overall F1 scores, thus reflecting better performance compared to standard machine learning based techniques and the state of the art deep learning based detection techniques for detecting and classifying the cyber-attacks.
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Researchers
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Seyed Alireza Bashiri Mosavi (Referee)
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