Transfer learning based intrusion detection scheme for Internet of vehicles

• Published in: Information sciences 2021-02, Vol.547, p.119-135
• Main Authors: Li, Xinghua, Hu, Zhongyuan, Xu, Mengfan, Wang, Yunwei, Ma, Jianfeng
• Format: Article
• Language: English
• Subjects: Internet of Vehicles; Intrusion detection; Transfer learning
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2020.05.130

As a new type of network, the types of attack in the Internet of Vehicles (IoV) are constantly emerging and changing. Consequently, the machine learning based intrusion detection model has to update to cope with new attacks. However, existing machine learning based IoV intrusion detection schemes require large amounts of labeled data to complete model updates. For new attacks, the IoV cloud is also difficult to identify in time, which requires a lot of labor and time cost in IoV. To solve above issue, this paper employs transfer learning and proposes two model update schemes based on whether the IoV cloud can timely provide a small amount of labeled data for a new attack. The first one is the cloud-assisted update scheme where the IoV cloud can provide a small amount of data. And the second one is the local update scheme where the IoV cloud cannot provide any labeled data timely. In this paper, the local update scheme obtains pseudo label of the unlabeled data in new attacks via pre-classifies and uses the pseudo-labeled data for multiple rounds of transfer learning. Then the vehicle can complete the update without obtaining any labeled data through the IoV cloud. The experimental results show that compared with the existing method, our two schemes have improved the detection accuracy by at least 23%.