Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks

Abstract

In this paper, we explore low-power custom quantised Multi-Layer Perceptrons (MLPs) as an Intrusion Detection System (IDS) for automotive controller area network (CAN). We utilise the FINN framework from AMD/Xilinx to quantise, train and generate hardware IP of our MLP to detect denial of service (DoS) and fuzzying attacks on CAN network, using ZCU104 (XCZU7EV) FPGA as our target ECU architecture with integrated IDS capabilities. Our approach achieves significant improvements in latency (0.12 ms per-message processing latency) and inference energy consumption (0.25 mJ per inference) while achieving similar classification performance as state-of-the-art approaches in the literature.

2 pages, 1 figure, 2 tables. arXiv admin note: text overlap with arXiv:2401.11030

References in corpus (4)

• FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
• MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles
• A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs
• Deep Learning-based Embedded Intrusion Detection System for Automotive CAN

Cited by in corpus (1)

• Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN