Enhancing ECU identification security in CAN networks using distortion modeling and neural networks - Hafeez, Azeem; Malik, Hafiz; Irtaza, Aun; Uddin, Md Zia; Noori, Farzan M. (Frontiers in Computer Science, 2024)
A novel technique for electronic control unit (ECU) identification is proposed in this paper to address security vulnerabilities of the controller area network (CAN) protocol. The reliable ECU identification has the potential to prevent spoofing attacks launched over the CAN due to the inconsideration of the message authentication. In this regard, we model the ECU-specific random distortion caused by the imperfections in the digital-to-analog converter and semiconductor impurities in the transmitting ECU for fingerprinting.Afterward, a 4-layered artificial neural networks (ANNs) is trained on the feature set to identify the transmitting ECU and the corresponding ECU pin. The ECU-pin identification is also a novel contribution of this work and can be used to avoid voltage-based attacks. We have evaluated our method using ANNs over a dataset generated through 7 ECUs with 6 pins having 185 records for each ECU and 40 records for each pin. The performance evaluation against state-of-the-art methods revealed that the proposed method achieved 99.4% accuracy for ECU-identification and 96.7% accuracy for pin-identification, which signifies the reliability of the proposed approach.