Work

Anomaly detection of CAN bus messages through analysis of ID sequences

Year: 2017

Type: conference-paper

Abstract: This paper proposes a novel intrusion detection algorithm that aims to identify malicious CAN messages injected by attackers in the CAN bus of modern vehicles. The proposed algorithm identifies anomalies in the sequence of messages that flow in the CAN bus and is characterized by small memory and computational footprints, that make it applicable to current ECUs. Its detection performance are demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle.

Source: IEEE Intelligent Vehicles Symposium

DOI: 10.1109/ivs.2017.7995934

Authors: Mirco Marchetti, Dario Stabili

Institution: University of Modena and Reggio Emilia

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W2739928414