RX-ADS: Interpretable Anomaly Detection Using Adversarial ML for Electric Vehicle CAN Data
Author:
Affiliation:
1. Computer Science, Virginia Commonwealth University, Richmond, VA, USA
2. Idaho National Laboratory, Idaho Falls, ID, USA
Funder
Department of Energy through the U.S. DOE Idaho Operations Office
Commonwealth Cyber Initiative, an Investment in the Advancement of Cyber Research and Development, Innovation and Workforce Development
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Mechanical Engineering,Automotive Engineering
Link
https://ieeexplore.ieee.org/ielam/6979/10339106/10192485-aam.pdf
Reference36 articles.
1. An Adversarial Approach for Explainable AI in Intrusion Detection Systems
2. Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks
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5. Survey of intrusion detection systems: techniques, datasets and challenges
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1. A comprehensive guide to CAN IDS data and introduction of the ROAD dataset;PLOS ONE;2024-01-22
2. DAdAE: Domain Adversarial Autoencoder Based In-Vehicle CAN Anomaly Detection;IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society;2023-10-16
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