Intrusion detection system using deep learning for in-vehicle security

Abstract

With the development of vehicle intelligence technology, the combination of network and vehicle becomes inevitable, which brings much convenience to people. At the same time, hackers can also use technical vulnerabilities to attack vehicles, leading to severe traffic accidents and even vehicle crash. Based on this situation, the vehicle information security protection techniques have drawn great attention from researchers. This paper studies the vehicle intrusion detection system (IDS) based on the neural network algorithm in deep learning, and uses gradient descent with momentum (GDM) and gradient descent with momentum and adaptive gain (GDM/AG) to improve the efficiency and accuracy of IDS. The accuracy and efficiency of the proposed model are validated and evaluated by using real vehicles at the end of the paper. Experiments show that the GDM/AG algorithm can achieve faster convergence in comparison with the GDM algorithm in vehicle anomaly detection, and can detect anomaly data at the level of milliseconds. At the same time, the proposed model can adapt itself to detect unknown attacks. The veracity rate ranges from 97% to 98% in directing the adaptation when facing unknown attack types.