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.

提出了一种基于 FPGA 的 ECU 方法，通过专用的现成硬件加速器实现深度卷积神经网络入侵检测模型，平均准确率超过 99％，在多个攻击数据集中的误检率为 0.64％，并且相比于 GPU 上的 IDS 实现，在能量消耗上降低了 94％，每个消息的处理延迟降低了 51.8％。