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Sep 8, 2022
An Effective Abnormal Behavior Detection Approach for In-Vehicle Networks Using Feature Selection and Classification Algorithm
Authors: Haojie Ji [email protected], Guiyue Kou [email protected], Wenquan Feng [email protected], Junjie Zhang [email protected], and Yinghui Wang [email protected]Author Affiliations
Publication: CICTP 2022
ABSTRACT
Abnormal detection has become an essential method of security protection in automotive. In order to meet the compatibility between electronic control units (ECUs), the data transmitted in controller area network (CAN) bus need to obey different protocols and specific communication rules. Generally, these rules can be learnt through statistics and used in the abnormal detection of in-vehicle networks. However, satisfactory abnormal detection performance is hard to guarantee when the in-vehicle network communication rules is relatively simple. To improve the detection performance comprehensively, this paper chooses the classification algorithm to carry out anomaly detection. Considering the particularity of in-vehicle networks, this paper proposes a classification algorithm based on the feature vectors of CAN bus packets. Combining with the feature vectors, the convolutional neural network (CNN) algorithm is used to realize high detection performance for in-vehicle networks. Moreover, the real vehicle experiment has verified the satisfactory results for the proposed classification algorithm.
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Published online: Sep 8, 2022
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1Hefei Innovation Research Institute, School of Electronic and Information Engineering, Beihang Univ., Hefei, China. Email: [email protected]
2School of Mechanical Engineering, Nanchang Institute of Technology, Nanchang, China. Email: [email protected]
3School of Electronic and Information Engineering, Beihang Univ., Beijing, China. Email: [email protected]
4Hefei Innovation Research Institute, Beihang Univ., Hefei, China. Email: [email protected]
5Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Beijing, China. Email: [email protected]
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.