Poster Abstract:Intrusion Detection for In-vehicle Networks Based on Parc-net Architecture

The Controller Area Network (CAN) serves as a pivotal communication protocol for Electronic Control Units (ECUs) in modern automotive systems. However, the increasing interconnectivity and sophistication of these ECUs introduce significant vulnerabilities, rendering in-vehicle networks susceptible to a variety of cyber threats. This work presents a multi-class classification model for intrusion detection within vehicle CAN networks, utilizing Bidirectional Long Short-Term Memory (BILSTM) and Parc-Net architectures. We also introduce a novel feature extraction module that computes the rate of ID changes, significantly enhancing the model's detection capabilities. The proposed method was evaluated on the CAR-HACKING dataset, demonstrating remarkable performance. The proposed Intrusion Detection System (IDS) greatly contributes to the security of vehicle CAN networks by facilitating real-time detection and localization of potential intrusions.