Deep Learning-based Embedded Intrusion Detection System for Automotive
  CAN
Shashwat Khandelwal Eashan Wadhwa Shreejith Shanker
Rising complexity of in-vehicle electronics is enabling new capabilities like
autonomous driving and active safety. However, rising automation also increases
risk of security threats which is compounded by lack of in-built security
measures in legacy networks like CAN, allowing attackers to observe, tamper and
modify information shared over such broadcast networks. Various intrusion
detection approaches have been proposed to detect and tackle such threats, with
machine learning models proving highly effective. However, deploying machine
learning models will require high processing power through high-end processors
or GPUs to perform them close to line rate. In this paper, we propose a hybrid
FPGA-based ECU approach that can transparently integrate IDS functionality
through a dedicated off-the-shelf hardware accelerator that implements a
deep-CNN intrusion detection model. Our results show that the proposed approach
provides an average accuracy of over 99% across multiple attack datasets with
0.64% false detection rates while consuming 94% less energy and achieving 51.8%
reduction in per-message processing latency when compared to IDS
implementations on GPUs.