Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

TLDR

Recent deep learning advances, such as unsupervised pre‑training with deep belief networks, improve detection accuracy over traditional ANN‑based IDS. The study proposes a deep neural network–based intrusion detection system to enhance in‑vehicle network security. The DNN is trained on probability‑based feature vectors extracted from CAN packets and outputs class probabilities to distinguish normal from attack traffic. Experimental results show the system delivers real‑time attack detection with a significantly higher detection ratio on the CAN bus.

Abstract

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.