A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid
  FPGAs
Shashwat Khandelwal Shreejith Shanker
Rising connectivity in vehicles is enabling new capabilities like connected
autonomous driving and advanced driver assistance systems (ADAS) for improving
the safety and reliability of next-generation vehicles. This increased access
to in-vehicle functions compromises critical capabilities that use legacy
invehicle networks like Controller Area Network (CAN), which has no inherent
security or authentication mechanism. Intrusion detection and mitigation
approaches, particularly using machine learning models, have shown promising
results in detecting multiple attack vectors in CAN through their ability to
generalise to new vectors. However, most deployments require dedicated
computing units like GPUs to perform line-rate detection, consuming much higher
power. In this paper, we present a lightweight multi-attack quantised machine
learning model that is deployed using Xilinx's Deep Learning Processing Unit IP
on a Zynq Ultrascale+ (XCZU3EG) FPGA, which is trained and validated using the
public CAN Intrusion Detection dataset. The quantised model detects denial of
service and fuzzing attacks with an accuracy of above 99 % and a false positive
rate of 0.07%, which are comparable to the state-of-the-art techniques in the
literature. The Intrusion Detection System (IDS) execution consumes just 2.0 W
with software tasks running on the ECU and achieves a 25 % reduction in
per-message processing latency over the state-of-the-art implementations. This
deployment allows the ECU function to coexist with the IDS with minimal changes
to the tasks, making it ideal for real-time IDS in in-vehicle systems.