| Title | U-CAN: A Convolutional Neural Network Based Intrusion Detection for Controller Area Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Desta, Araya Kibrom, Ohira, Shuji, Arai, Ismail, Fujikawa, Kazutoshi |
| Conference Name | 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) |
| Keywords | automotive, CAN bus, controller area network, convolutional neural networks, Data models, Gears, Hamming distance, Human Behavior, human factors, in-vehicle network security, Intrusion detection, Neural networks, pubcrawl, resilience, Resiliency, security, Software |
| Abstract | The Controller area network (CAN) is the most extensively used in-vehicle network. It is set to enable communication between a number of electronic control units (ECU) that are widely found in most modern vehicles. CAN is the de facto in-vehicle network standard due to its error avoidance techniques and similar features, but it is vulnerable to various attacks. In this research, we propose a CAN bus intrusion detection system (IDS) based on convolutional neural networks (CNN). U-CAN is a segmentation model that is trained by monitoring CAN traffic data that are preprocessed using hamming distance and saliency detection algorithm. The model is trained and tested using publicly available datasets of raw and reverse-engineered CAN frames. With an F\_1 Score of 0.997, U-CAN can detect DoS, Fuzzy, spoofing gear, and spoofing RPM attacks of the publicly available raw CAN frames. The model trained on reverse-engineered CAN signals that contain plateau attacks also results in a true positive rate and false-positive rate of 0.971 and 0.998, respectively. |
| Notes | ISSN: 0730-3157 |
| DOI | 10.1109/COMPSAC54236.2022.00235 |
| Citation Key | desta_u-can_2022 |
U-CAN: A Convolutional Neural Network Based Intrusion Detection for Controller Area Networks