A Lightweight Intrusion Detection System on In-Vehicle Network Using Polynomial Features

Abstract

Intrusion detection systems are critical to maintaining network security, especially for onboard vehicle networks. However, the complexity of existing systems often requires high computational power, making them impractical for many applications. In this study, we propose a simple yet effective approach for intrusion detection based on polynomial feature processing and a lightweight deep learning model. Our approach achieves significantly higher accuracy than other computationally intensive deep learning models, making it a practical solution for intrusion detection in low-resource environments. Our proposed method can be easily implemented and integrated into existing systems, providing a lightweight yet effective solution for network security.