| Title | Model-Based Deep Learning for Cyber-Attack Detection in Electric Drive Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Jawdeh, Shaya Abou, Choi, Seungdeog, Liu, Chung-Hung |
| Conference Name | 2022 IEEE Applied Power Electronics Conference and Exposition (APEC) |
| Date Published | mar |
| Keywords | command injection attacks, composability, Conferences, control systems, Current measurement, cyber-attack detection, Cyber-physical systems, Deep Learning, Detectors, electric drive systems, Metrics, power electronics, pubcrawl, resilience, Resiliency, Training |
| Abstract | Modern cyber-physical systems that comprise controlled power electronics are becoming more internet-of-things-enabled and vulnerable to cyber-attacks. Therefore, hardening those systems against cyber-attacks becomes an emerging need. In this paper, a model-based deep learning cyber-attack detection to protect electric drive systems from cyber-attacks on the physical level is proposed. The approach combines the model physics with a deep learning-based classifier. The combination of model-based and deep learning will enable more accurate cyber-attack detection results. The proposed cyber-attack detector will be trained and simulated on a PM based electric drive system to detect false data injection attacks on the drive controller command and sensor signals. |
| Notes | ISSN: 2470-6647 |
| DOI | 10.1109/APEC43599.2022.9773385 |
| Citation Key | jawdeh_model-based_2022 |
Model-Based Deep Learning for Cyber-Attack Detection in Electric Drive Systems