Nowadays, with the rapidly increasing of connected vehicles more and more cyber-attacks, which could compromise the driving experience, are possible. Indeed, connected vehicles are not able to guarantee the information security and shown their vulnerabilities in terms of Confidentiality, Integrity and Availability (CIA). These vulnerabilities advise the inefficiency of modem vehicles which, due to the advent of the Internet of Things paradigm, are equipped with many Internet access points. In this paper, we propose an approach based on Intrusion Detection System (IDS), which use a Machine Learning technique through Bayesian Networks approach to detect possible attacks on Controller Area Network Bus (CAN-Bus). In this way, a framework, which takes advantage of an embedded system able to discover a non-linear messages flow on CAN-Bus, is presented.

EIDS: Embedded intrusion detection system using machine learning to detect attack over the can-bus

Lombardi M.;Pascale F.;Santaniello D.
2020

Abstract

Nowadays, with the rapidly increasing of connected vehicles more and more cyber-attacks, which could compromise the driving experience, are possible. Indeed, connected vehicles are not able to guarantee the information security and shown their vulnerabilities in terms of Confidentiality, Integrity and Availability (CIA). These vulnerabilities advise the inefficiency of modem vehicles which, due to the advent of the Internet of Things paradigm, are equipped with many Internet access points. In this paper, we propose an approach based on Intrusion Detection System (IDS), which use a Machine Learning technique through Bayesian Networks approach to detect possible attacks on Controller Area Network Bus (CAN-Bus). In this way, a framework, which takes advantage of an embedded system able to discover a non-linear messages flow on CAN-Bus, is presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4770502
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