Скачать или смотреть Low-Cost Intrusion Detection System for CAN BusNetworks Using RISC-V with Binarised Neural Networks

The increasing number of sensors used in smart
vehicles has significantly raised concerns about in-vehicle network
security. Machine learning (ML) based Intrusion Detection
System (IDS) designed for the Controller Area Network
(CAN) protocol demonstrates excellent performance in detecting
attacks without introducing additional overhead. Deploying such
IDSs on FPGA using RISC-V, rather than directly deploying
the model on FPGA, sacrifices detection speed but in general
reduces energy consumption and on-chip resource usage. The
RISC-V platform deployed on FPGA can handle tasks beyond
attack detection, offering greater flexibility, particularly for lowcost
FPGA devices. This work leverages the lightweight Zero-
Riscy core to deploy a Binary Neural Network (BNN)-based
IDS on FPGA. Three custom instructions are proposed for
convolution, pooling, and fully connected layers, integrating
XNOR, POPCOUNT, and threshold operations. This reduces the
number of instructions required per task, thereby decreasing the
frequency of interactions between Zero-Riscy and the instruction
memory. The results show that the Zero-Riscy-based IDS, while
sacrificing some detection speed, still meets real-time detection
requirements. Compared to the state-of-the-art FPGA-based IDS
implementations, it achieves energy consumption at 13% and
resource utilisation at 20% of their levels.
Keywords—RISC-V, Intrusion Detection System, Controller
Area Network, Machine Learning, Binarised Neural Network,
FPGA