Controller Area Network (CAN) is the de facto standard for in-vehicle networks. However, it is inherently vulnerable to various attacks due to the lack of security features. Intrusion detection systems (IDSs) are considered effective approaches to protect in-vehicle networks. IDSs based on advanced deep learning algorithms have been proposed to achieve higher detection accuracy. However, those systems generally involve high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory and energy costs, we propose a new IDS system using Binarized Neural Network (BNN). Compared to full-precision counterparts, BNNs can offer faster detection, smaller memory cost, and lower energy consumption. Moreover, BNNs can be further accelerated by leveraging Field-Programmable Grid Arrays (FPGAs) since BNNs cut down the hardware consumption. The proposed IDS is based on a BNN model that suits CAN traffic messages and takes advantage of sequential features of messages rather than each individual message. We also explore various design choices of BNN, including increasing network width and depth, to improve accuracy as BNNs typically sacrifice accuracy. The performance of our IDS is evaluated with four different real vehicle datasets. Experimental results show that the proposed IDS reduces the detection latency (3 times faster) on the same CPU platform while maintaining acceptable detection rates compared with full-precision models. We also examine the proposed IDS on multiple platforms, and our results show that using FPGA hardware reduces the detection latency dramatically (128 times faster) with lower power consumption compared to an embedded CPU device. Furthermore, we evaluate BNNs with different designs. Our experimental results demonstrate that wider or deeper models definitely improve accuracy at the cost of increased latency and model sizes to varying degrees. Applications are recommended to choose the appropriate model design they need depending on available resources they have.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.