FIVADMI: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation
Mahbub, Khaled and Nehme, Antonio and Patwary, Mohammad and Lacoste, Mark and Allio, Sylvain (2024) FIVADMI: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation. Future Internet, 16 (8). ISSN 1999-5903
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Abstract
Self-driving vehicles have attracted significant attention in the automotive industry that is heavi-ly investing to reach the level of reliability needed from these safety critical systems. Security of in-vehicle communications is mandatory to achieve this goal. Most of the existing research to de-tect anomalies for in-vehicle communication does not take into account the low processing power of the in-vehicle Network and ECUs (Electronic Control Units). Also, these approaches do not consider system level isolation challenges such as side-channel vulnerabilities, that may arise due to adoption of new technologies in the automotive domain. This paper introduces and discusses the design of a framework to detect anomalies in in-vehicle communications, including side channel attacks. The proposed framework supports real time monitoring of data exchanges among the components of in-vehicle communication network and ensures the isolation of the components in in-vehicle network by deploying them in Trusted Execution Environments (TEEs). The framework is designed based on the AUTOSAR open standard for automotive software ar-chitecture and framework. The paper also discusses the implementation and evaluation of the proposed framework.
Item Type: | Article |
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Identification Number: | 10.3390/fi16080288 |
Dates: | Date Event 2 August 2024 Accepted 8 August 2024 Published Online |
Uncontrolled Keywords: | AUTOSAR, ECU, Isolation, Resilience, System Security |
Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science |
Divisions: | Faculty of Computing, Engineering and the Built Environment > College of Computing |
Depositing User: | Gemma Tonks |
Date Deposited: | 06 Aug 2024 14:29 |
Last Modified: | 03 Sep 2024 15:34 |
URI: | https://www.open-access.bcu.ac.uk/id/eprint/15687 |
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