基于证据深度学习的CAN网络入侵检测框架

doi:10.19562/j.chinasae.qcgc.2024.11.010

Abstract

Abstract:

With the continuous development of mobile communication technologies in intelligent autonomous driving systems， securing vehicular communication data has become pivotal for transportation safety. Faced with threats of hackers remotely manipulating vehicles through the CAN bus network， existing frameworks can detect known attacks but falter in identifying location-based attacks. A detection framework integrating evidence-based deep learning is proposed in this paper， comprising data preprocessing， analysis， and attack detection modules. The preprocessing module employs independent hot encoding to enhance data quality and adaptability. The analysis module utilizes Generative Adversarial Networks （GANs） to bolster the framework's generalization and simulate attack scenarios. The attack detection module harnesses evidence-based deep learning to enhance the framework's capability in handling uncertainties from unknown attacks.The framework is tested on an open-source car hacking dataset and a dataset constructed based on the Chery EXEED RX model. The test results show that the framework improves the overall performance by 24.5% in detecting unknown attacks compared to traditional classification probability-based networks.

Key words: intrusion detection, evidence deep learning, uncertainty, loss function

Qin Shi,Zhiwei Li,Teng Cheng,Qiang Zhang,Wenchong Wang. Intrusion Detection Framework for CAN Networks Based on Evidence Deep Learning[J].Automotive Engineering, 2024, 46(11): 2039-2045.

Figures/Tables 8

References 20

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攻击类型	消息总数	正常消息	攻击消息
DoS攻击	3 665 771	3 078 250	587 521
Fuzzy攻击	3 838 860	3 347 013	491 847
GEAR攻击	4 443 142	3 845 890	597 252
RPM攻击	4 621 702	3 845 890	654 897
正常消息	988 987	988 987	0

攻击类型	消息总数	正常消息	攻击消息
DoS攻击	3 298 815	2 985 348	313 467
Fuzzy攻击	3 454 577	3 087 217	367 360
GEAR攻击	3 998 368	3 645 241	353 127
RPM攻击	4 159 054	3 904 785	254 269
正常消息	889 986	889 986	0

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Intrusion Detection Framework for CAN Networks Based on Evidence Deep Learning

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