Design and Test of an Embedded Real-Time Compact Voltage Fingerprinting Algorithm for Enhanced Automotive Cybersecurity

Abstract

The article proposes an algorithm for monitoring data traffic in CAN networks on board vehicles. This algorithm detects cyber-attacks through statistical analysis of voltage samples from the protocol’s physical layer. The method aims to be compact and achieve real-time throughput for implementation on an embedded platform. In the article are shown tests of the proposed method on the Automotive MCU AURIX TC375. The Electronic Control Unit (ECU) classification algorithm results from the re-elaboration of the K-Nearest Neighbor (KNN) method. Using.dbc and recorded.asc traces acquired from a real vehicle (Giulietta Alfa Romeo model), the message traffic is reconstructed and replicated via experimental prototype, providing different voltage levels for each device used to emulate the ECU. The article evaluates the algorithm performance through extensive experiments, assessing its ability to detect traffic anomalies in various attack scenarios.