文章基本信息

标题：異なる車種をまたいだ車載ネットワークへの攻撃検知
本地全文：下载
作者：中村周 ; 竹内孝 ; 鹿島久嗣等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2022
卷号：37
期号：5
页码：1-10
DOI：10.1527/tjsai.37-5_C-M42
语种：Japanese
出版社：The Japanese Society for Artificial Intelligence
摘要：Recent studies have demonstrated that the injection of malicious messages into in-vehicle networks can cause unintended operation of the controls of vehicles, which has been highlighted as one of the most serious and urgent issues that threaten the safety of automobiles. Attempts have been made to use supervised and unsupervised machine learning for automatic, data-driven intrusion detection. However, previous approaches considered only the detection and classification of attacks on a target car based on the data of the same model of car; they are relatively ineffective when the objective is to handle new car models for which not many data are yet available. In this paper, we address the task of detecting and classifying malicious messages injected into in-vehicle networks by transferring “knowledge” from different car models for which ample data exist. In our proposed approach to the dataset of new car models a pretrained classification model that was supervised-trained on the data of previous car models is combined it with an unsupervised detector that uses only the normal data from the target car. The advantage of the proposed approach is that it does not require past data and therefore is applicable to various scenarios. The results of our experiments using in-vehicle CAN messages datasets collected from three different cars show the effectiveness of the proposed approach.