Electric Vehicles CAN Bus Cyber Attacks Detection Using Adaptive Neuro Fuzzy Inference System

The Electric Vehicle (EV) industry has recently experienced notable technological progress in the field of Controller Area Network (CAN) protocol. 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 cyber 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 model testing accuracy is 99.6%, TPR and TNR values are above 99.8%. In addition to the low FPR and FNR values are less than 0.2% of the proposed ANFIS-SC and ANFIS-FCM detection techniques. The overall F1 score is above 98.8%.