Intrusion detection system using SOEKS and deep learning for in-vehicle security

Abstract

With the continuous development of the intelligent vehicle, vehicle security events occur frequently, therefore, the vehicle information security is particularly important. In this paper, the in-vehicle security measures are analyzed, especially the current situation of in-vehicle intrusion detection system, which are mainly aimed at specific vehicles and are not enough to meet the need of vehicle security. Then, a new in-vehicle intrusion detection mechanism is proposed based on deep learning and the set of experience knowledge structure (SOEKS), which is a knowledge representation structure. Utilizing SOEKS and information entropy to increase the versatility of intrusion detection for different vehicle. In practice, the more precise model for specific vehicle can formed by training a large amount of specific vehicle data through deep learning. It is demonstrated with experimental results that the proposed approach is able to have 98% accuracy and detect a wide range of in-vehicle attacks.