Abstract: With the rapid development and pervasive deployment of the Internet of Vehicles (IoV),it provides the services of Internet and big data analytics to the intelligent and connected vehicles,while incurs the issues of security and privacy.The closure of traditional in-vehicle networks leads to the communications protocols,particularly,the most commonly applied controller area network (CAN) bus protocol,lack of security and privacy protection mechanisms.Thus,to detect the network intrusions and protect the vehicles from being attacked,a support vector data description (SVDD) based intrusion detection method is proposed in this paper.Specifically,the weighted self-information of message IDs and the normalized values of IDs are selected as features for SVDD modeling,and the SVDD models are trained at the mobile edge computing (MEC) servers.The vehicles use the trained SVDD models for identifying the abnormal values of the selected features to detect the network intrusions.Simulations are conducted based on the CAN network dataset published by the HCR Lab of Korea University,where three conventional information entropy based in-vehicle network intrusion detection methods are adopted as the benchmarks.As compared to the benchmarks,the proposed method has dramatically improved the intrusion detection accuracy,especially when the number of intruded messages is small.

Key words: Internet of Vehicles, Mobile edge computing, In-vehicle network, Network intrusion detection, Support vector data description algorithm