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Research Article
Dec 14, 2021

Two-Step Algorithm to Detect Cyber-Attack Over the Can-Bus: A Preliminary Case Study in Connected Vehicles

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 8, Issue 3

Abstract

Modern vehicles are connected to the network and between each other through smart sensors and smart objects commonly present on board. This situation has allowed manufacturers to send over-the-air updates, receive diagnostic information, and offer various multimedia services. More generally, at present, all this is indicated by the term “Vehicle to Everything” (V2X), which indicates a system of communication between a vehicle and any entity that may influence the vehicle and vice versa. However, it introduces problems regarding the vehicle's IT security. It is possible, for example, by tampering with one of the electronic control units (ECUs) to take partial or total control of the vehicle. In this paper, we introduce a preliminary study case of a probabilistic approach in an intrusion detection system over the CAN-bus to guarantee cybersecurity inside connected vehicles. In particular, through the use of an innovative two-step detection algorithm that exploits both the variation of the status parameters of the various ECUs over time and the Bayesian networks can identify a possible attack. Starting from a domain analysis is possible to find out what are the parameters of interests and how these are related to each other. The first experimental results seem encouraging. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4052823.

Information & Authors

Information

Published In

Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 8Issue 3September 2022

History

Received: Mar 9, 2021
Revision received: Oct 20, 2021
Published online: Dec 14, 2021
Published in print: Sep 1, 2022

Authors

Affiliations

Marco Lombardi [email protected]
Department of Industrial Engineering (DIIn), University of Salerno Via Giovanni Paolo II, Fisciano (SA) 132 - 84084, Italy e-mail: [email protected]
Francesco Pascale [email protected]
Almaviva S.p.A., Via di Casal Boccone, Roma 188-190 - 00137, Italy e-mail: [email protected]
Domenico Santaniello [email protected]
Department of Industrial Engineering (DIIn), University of Salerno Via Giovanni Paolo II, Fisciano (SA) 132-84084, Italy e-mail: [email protected]

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