Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN
Citation:
Shashwat Khandelwal & Shanker Shreejith, Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN, International Conference on Field Programmable Logic and Applications (FPL), September, 2023Abstract:
Vehicles today comprise intelligent systems like
connected autonomous driving and advanced driving assistance
systems (ADAS) to enhance the driving experience, which is
enabled through increased connectivity to infrastructure and
fusion of information from different sensing modes. However, the
rising connectivity coupled with the legacy network architecture
within vehicles can be exploited for launching active and passive
attacks on critical vehicle systems and directly affecting the
safety of passengers. Machine learning-based intrusion detection
models have been shown to successfully detect multiple targeted
attack vectors in recent literature, whose deployments are en-
abled through quantised neural networks targeting low-power
platforms. Multiple models are often required to simultaneously
detect multiple attack vectors, increasing the area, (resource)
cost, and energy consumption. In this paper, we present a case
for utilising custom-quantised MLP’s (CQMLP) as a multi-class
classification model, capable of detecting multiple attacks from
the benign flow of controller area network (CAN) messages.
The specific quantisation and neural architecture are determined
through a joint design space exploration, resulting in our choice
of the 2-bit precision and the n-layer MLP. Our 2-bit version
is trained using Brevitas and optimised as a dataflow hardware
model through the FINN toolflow from AMD/Xilinx, targeting
an XCZU7EV device. We show that the 2-bit CQMLP model,
when integrated as the IDS, can detect malicious attack messages
(DoS, fuzzing, and spoofing attack) with a very high accuracy of
99.9%, on par with the state-of-the-art methods in the literature.
Furthermore, the dataflow model can perform line rate detection
at a latency of 0.11 ms from message reception while consuming
0.23 mJ/inference, making it ideally suited for integration with
an ECU in critical CAN networks.
Index Terms—
Author's Homepage:
http://people.tcd.ie/shankersDescription:
IN_PRESS
Author: Shanker, Shreejith
Other Titles:
International Conference on Field Programmable Logic and Applications (FPL)Type of material:
Conference PaperAvailability:
Full text availableMetadata
Show full item recordLicences: