Anomaly Intrusion Detection for CAN-FD Bus by Support Vector Machine

被引：0

作者：

Luo F. ^{[1
]}

Hu Q. ^{[1
]}

Hou S. ^{[1
]}

Zhang X. ^{[1
]}

机构：

[1] School of Automotive Studies, Tongji University, Shanghai

来源：

Hu, Qiang (404huqiang@tongji.edu.cn) | 1790年 / Science Press卷 / 48期

关键词：

Cyber security; Intrusion detection; Support vector machine; The controller area network with flexible data-rate (CAN-FD);

D O I：

10.11908/j.issn.0253-374x.20004

中图分类号：

学科分类号：

摘要：

Aiming at the potential network attack for vehicles, an abnormal intrusion detection algorithm based on support vector machine is proposed for the controller area network with flexible data-rate (CAN-FD) bus. With the framework of common intrusion detection framework (CIDF), the method uses message identifier (ID), time period and data field as intrusion detection features. Using the binary classification and small sample features of the support vector machine algorithm, the identification of intrusion message data in CAN-FD network environment is realized. The simulation data show that the proposed method has a high accuracy of intrusion detection, and this method can be used for periodic packets and aperiodic messages. © 2020, Editorial Department of Journal of Tongji University. All right reserved.

引用

页码：1790 / 1796

页数：6

共 16 条

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