| Title | A Hybrid Approach for Fast Anomaly Detection in Controller Area Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sunny, Jerin, Sankaran, Sriram, Saraswat, Vishal |
| Conference Name | 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) |
| Keywords | anomaly detection, automobiles, Automotive Anomaly Detection, controller area network, controller area network security, Cyber-physical systems, feature extraction, in-vehicle network security, Internet of Things, pubcrawl, Resiliency, Time factors, Timing, vehicular ad hoc networks, Wireless communication |
| Abstract | Recent advancements in the field of in-vehicle network and wireless communication, has been steadily progressing. Also, the advent of technologies such as Vehicular Adhoc Networks (VANET) and Intelligent Transportation System (ITS), has transformed modern automobiles into a sophisticated cyber-physical system rather than just a isolated mechanical device. Modern automobiles rely on many electronic control units communicating over the Controller Area Network (CAN) bus. Although protecting the car's external interfaces is an vital part of preventing attacks, detecting malicious activity on the CAN bus is an effective second line of defense against attacks. This paper proposes a hybrid anomaly detection system for CAN bus based on patterns of recurring messages and time interval of messages. The proposed method does not require modifications in CAN bus. The proposed system is evaluated on real CAN bus traffic with simulated attack scenarios. Results obtained show that our proposed system achieved a good detection rate with fast response times. |
| DOI | 10.1109/ANTS50601.2020.9342791 |
| Citation Key | sunny_hybrid_2020 |
A Hybrid Approach for Fast Anomaly Detection in Controller Area Networks