An Intrusion Detection System Based on Machine Learning for CAN-Bus

被引：19

作者：

Tian, Daxin ^{[1
,3
,4
]}

Li, Yuzhou ^{[1
,3
,4
]}

Wang, Yunpeng ^{[1
,3
,4
]}

Duan, Xuting ^{[3
]}

Wang, Congyu ^{[3
]}

Wang, Wenyang ^{[2
]}

Hui, Rong ^{[2
]}

Guo, Peng ^{[2
]}

机构：

[1] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, XueYuan Rd 37, Beijing 100191, Peoples R China

[2] China Automot Technol & Res Ctr, Automot Engn Res Inst, East Xianfeng Rd 68, Tianjin 300300, Peoples R China

[3] Beihang Univ, Sch Transportat Sci & Engn, Beijing Key Lab Cooperat Vehicle Infrastruct Syst, XueYuan Rd 37, Beijing 100191, Peoples R China

[4] Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Si Pai Lou 2, Nanjing 210096, Jiangsu, Peoples R China

来源：

INDUSTRIAL NETWORKS AND INTELLIGENT SYSTEMS, INISCOM 2017 | 2018年 / 221卷

关键词：

CAN-Bus; Information security; IDS; Machine learning; GBDT; Entropy; Detection performance; ATTACK;

D O I：

10.1007/978-3-319-74176-5_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The CAN-Bus is currently the most widely used vehicle bus network technology, but it is designed for needs of vehicle control system, having massive data and lacking of information security mechanisms and means. The Intrusion Detection System (IDS) based on machine learning is an efficient active information security defense method and suitable for massive data processing. We use a machine learning algorithm-Gradient Boosting Decision Tree (GBDT) in IDS for CAN-Bus and propose a new feature based on entropy as the feature construction of GBDT algorithm. In detection performance, the IDS based on GBDT has a high True Positive (TP) rate and a low False Positive (FP) rate.

引用

页码：285 / 294

页数：10

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