Deep Learning for In-Vehicle Intrusion Detection System

被引:2
作者
Gherbi, Elies [1 ,2 ]
Hanczar, Blaise [2 ]
Janodet, Jean-Christophe [2 ]
Klaudel, Witold [1 ]
机构
[1] Inst Res Syst X, F-91120 Palaiseau, France
[2] Univ Evry, Univ Paris Saclay, IBISC, St Aubin, France
来源
NEURAL INFORMATION PROCESSING, ICONIP 2020, PT IV | 2020年 / 1332卷
关键词
Intrusion detection system; In-vehicle security; Deep learning; Anomaly detection; Time series;
D O I
10.1007/978-3-030-63820-7_6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Modern and future vehicles are complex cyber-physical systems. The connection to their outside environment raises many security problems that impact our safety directly. In this work, we propose a Deep CAN intrusion detection system framework. We introduce a multivariate time series representation for asynchronous CAN data which enhances the temporal modelling of deep learning architectures for anomaly detection. We study different deep learning tasks (supervised/unsupervised) and compare several architectures, in order to design an in-vehicle intrusion detection system that fits in-vehicle computational constraints. We conduct experiments with many types of attacks on an in-vehicle CAN using SynCAn Dataset.
引用
收藏
页码:50 / 58
页数:9
相关论文
共 19 条
[1]  
Avatefipour O., 2018, State-of-the-art survey on in-vehicle network communication (can-bus) security and vulnerabilities
[2]  
Bagnall A.J., 2016, CoRR abs/1602.01711
[3]  
Bai S., 2018, CoRR
[4]  
Dupont G., 2019, CoRR
[5]   An overview of Controller Area Network [J].
Farsi, M ;
Ratcliff, K ;
Barbosa, M .
COMPUTING & CONTROL ENGINEERING JOURNAL, 1999, 10 (03) :113-120
[6]  
Hanselmann M., 2019, UNSUPERVISED INTRUSI
[7]  
Hochreiter S., 1997, NEURAL COMPUT, V9, P1735, DOI DOI 10.1162/NECO.1997.9.8.1735
[8]   Security threats to automotive CAN networks-Practical examples and selected short-term countermeasures [J].
Hoppe, Tobias ;
Kiltz, Stefan ;
Dittmann, Jana .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2011, 96 (01) :11-25
[9]   Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security [J].
Kang, Min-Joo ;
Kang, Je-Won .
PLOS ONE, 2016, 11 (06)
[10]   Experimental Security Analysis of a Modern Automobile [J].
Koscher, Karl ;
Czeskis, Alexei ;
Roesner, Franziska ;
Patel, Shwetak ;
Kohno, Tadayoshi ;
Checkoway, Stephen ;
Mccoy, Damon ;
Kantor, Brian ;
Anderson, Danny ;
Shacham, Hovav ;
Savage, Stefan .
2010 IEEE SYMPOSIUM ON SECURITY AND PRIVACY, 2010, :447-462