Toward a Real-Time Intrusion Detection System for Modern In-Vehicle Networks

Artikel i vetenskaplig tidskrift, 2025

Over the past decade, it has been demonstrated that the In-Vehicle Network (IVN) of a modern Intelligent Transportation System (ITS) is vulnerable to several cyberattacks. Given the collaborative nature of these systems, detecting (remote) cyberattacks is of utmost importance in ensuring trusted interactions. One key technique that has been explored to detect adversarial presence in IVNs are Intrusion Detection Systems (IDSs). However, many existing solutions focus on legacy architectures or are not practically feasible due to their hardware requirements or inability to operate in real-time. To this end, we propose Modular Reduced Temporal Convolutional Network (MR-TCN), an efficient IDS architecture that can effectively be accelerated on hardware to enable real-time intrusion detection in low-cost embedded platforms. Additionally, we evaluate variants of MR-TCN on a Field-Programmable Gate Array (FPGA) platform across a diverse range of IVN traffic (i.e., CAN CC, CAN FD, and Automotive Ethernet), demonstrating its suitability in real-world applications.