| Title | ECU Identification using Neural Network Classification and Hyperparameter Tuning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Verma, Kunaal, Girdhar, Mansi, Hafeez, Azeem, Awad, Selim S. |
| Conference Name | 2022 IEEE International Workshop on Information Forensics and Security (WIFS) |
| Keywords | artificial neural network (ANN), automotive electronics (AE), controller area network, electronic control unit, feature extraction, Fingerprint recognition, frequency-domain analysis, Human Behavior, human factors, IDS, Intrusion detection, machine learning (ML), Neural networks, pubcrawl, resilience, Resiliency, security, telecommunication traffic, Training |
| Abstract | Intrusion detection for Controller Area Network (CAN) protocol requires modern methods in order to compete with other electrical architectures. Fingerprint Intrusion Detection Systems (IDS) provide a promising new approach to solve this problem. By characterizing network traffic from known ECUs, hazardous messages can be discriminated. In this article, a modified version of Fingerprint IDS is employed utilizing both step response and spectral characterization of network traffic via neural network training. With the addition of feature set reduction and hyperparameter tuning, this method accomplishes a 99.4% detection rate of trusted ECU traffic. |
| Notes | ISSN: 2157-4774 |
| DOI | 10.1109/WIFS55849.2022.9975396 |
| Citation Key | verma_ecu_2022 |
ECU Identification using Neural Network Classification and Hyperparameter Tuning