TinyML-Based Intrusion Detection System for In-Vehicle Network Using Convolutional Neural Network on Embedded Devices
Hyungchul Im, Seongsoo Lee
Abstract
This letter proposes a novel model for effectively detecting malicious messages in controller area network (CAN) communication, which is widely used in automotive networks. Because in-vehicle networks operate in resource-constrained environments, an intrusion detection system (IDS) must simultaneously provide a low computational load and excellent detection performance. However, existing models are unsuitable for deployment on low-power embedded devices owing to their high computational requirements. This letter presents a low-complexity convolutional neural network (CNN)-based IDS for deployment on embedded edge devices. The proposed model applies CNN operations separately to the CAN ID sequence and the data field of the CAN frame to extract features and concatenate them for feature fusion. Experimental results demonstrate that this approach requires considerably less computational load and provides superior detection performance. Furthermore, the proposed model is deployed on a resource-constrained nRF52840 microcontroller using TensorFlow Lite for Microcontrollers with 20.44-kB flash memory and 26.44-kB RAM without quantization.