FIVADMI: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation

Khaled Mahbub (), Antonio Nehme, Mohammad Patwary, Marc Lacoste and Sylvain Allio
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Khaled Mahbub: College of Computing, Birmingham City University, Birmingham B5 5JU, UK
Antonio Nehme: College of Computing, Birmingham City University, Birmingham B5 5JU, UK
Mohammad Patwary: Digital Innovation & Solution Centre, University of Wolverhampton, Wolverhampton WV1 1LY, UK
Marc Lacoste: Department of Security, Orange Labs, 38240 Meylan, France
Sylvain Allio: Department of Security, Orange Labs, 38240 Meylan, France

Future Internet, 2024, vol. 16, issue 8, 1-28

Abstract: Self-driving vehicles have attracted significant attention in the automotive industry that is heavily 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 detect 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 architecture and framework. The paper also discusses the implementation and evaluation of the proposed framework.

Keywords: AUTOSAR; ECU; isolation; resilience; system security (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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