Quantised Neural Network Accelerators for Low-Power IDS in Automotive
  Networks
Shashwat Khandelwal Anneliese Walsh Shanker Shreejith
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.