Shallow Learning-Based Intrusion Detection System for In-Vehicle Network: ASIC Implementation

Abstract

This paper presents an Application-Specific Integrated Circuit (ASIC) implementation and Field-Programmable Gate Array (FPGA) verification of a Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) designed to enhance the security of an in-vehicle Controller Area Network (CAN) BUS and detect malicious messages. The CNN model employs a lightweight architecture with a single convolution layer using a 2 × 2 kernel and integrates a filter algorithm optimized for Fuzzy and Spoofing attacks to improve the performance. The IDS is implemented on an Electronic Control Unit platform powered by an ARM Cortex-M3 core and uses SRAM to store the parameters utilized by the CNN model and filter algorithm, targeting ASIC implementation with TSMC 180 nm technology. Functional verification was conducted by configuring a simplified CAN bus environment using the Xilinx Nexys Video FPGA and PEAK-System PCAN-USB, which was validated in real-time against DoS, Spoofing, and Fuzzy attack scenarios. The proposed lightweight CNN-based IDS achieved a fast detection speed of 0.0233 ms and an average accuracy of 99.6879%, thereby demonstrating its potential to enhance the security of in-vehicle CAN BUS.