Machine Learning based ECU Detection for Automotive Security

Due to digital transformation, an autonomous vehi-cle (AV) is realized as a network of multiple electronic control units (ECUs) for providing ubiquitous connectivity and control-ling various electronic functions ranging from essential safety (power steering, airbags) to comfort (driver or passenger seats), to security and access (keyless entry). Out of different commu-nication busses, controller area network (CAN) is a cardinal bus protocol used as a real-time communication interface between these different electronic devices or ECUs embedded in a vehicle. However, an insufficient security design of CAN bus has rendered the network to be vulnerable to innumerable cyber-attacks and risks, hence jeopardizing its cybersecurity. To address the security issues, it is predominant to realize the malicious ECUs in an in-vehicle CAN bus network. Therefore, this paper proposes a novel ECU fingerprinting technique, where unique digital signatures extracted as a result of intrinsic characteristics of the ECUs are used to detect the ECU liable for broadcasting counterfeit messages received on the CAN bus. Further, the proposed work analyzes the data from seven distinct ECUs by employing three machine learning (ML) algorithms, i.e., k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Logistic Regression (LR). Further, the performance of the proposed cybersecurity framework is evaluated and compared using the above-mentioned algorithms.