A Real-time Anomaly-based Intrusion Detection System for Automotive Controller Area Networks


The Controller Area Network (CAN) is the most pervasive in-vehiclenetwork technology in cars. However, since CAN was designed with no securityconcerns, solutions to mitigate cyber attacks on CAN networks have been pro-posed. Prior works have shown that detecting anomalies in the CAN networktraffic is a promising solution for increasing vehicle security. One of the mainchallenges in preventing a malicious CAN frame transmission is to be able todetect the anomaly before the end of the frame. This paper presents a real-timeanomaly-based Intrusion Detection System (IDS) capable of meeting this dead-line by using the Isolation Forest detection algorithm implemented in a hardwaredescription language. A true positive rate higher than 99% is achieved in testscenarios. The system requires less than 1μs to evaluate a frame’s payload, thusbeing able to detect the anomaly before the end of the frame.

Palavras-chave: Intrusion Detection Systems, Controller Area Network, Automotive networks, Real-time systems, Isolation Forest


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PRADO D'ANDRADA, Luís Felipe; FREITAS DE ARAUJO-FILHO, Paulo; CAMPELO, Divanilson Rodrigo. A Real-time Anomaly-based Intrusion Detection System for Automotive Controller Area Networks. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 658-671. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2020.12316.