Adaptive Optimization Method for Controller Area Network Anomaly Detection under Vehicle Resource Constraints

ZHANG Jinfeng; ZHANG Zhen; LIU Shaoxun; WU Jiangxing

doi:10.11999/JEIT220692

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2432-2442

ZHANG Jinfeng, ZHANG Zhen, LIU Shaoxun, WU Jiangxing. Adaptive Optimization Method for Controller Area Network Anomaly Detection under Vehicle Resource Constraints[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2432-2442. doi: 10.11999/JEIT220692

Citation:

ZHANG Jinfeng, ZHANG Zhen, LIU Shaoxun, WU Jiangxing. Adaptive Optimization Method for Controller Area Network Anomaly Detection under Vehicle Resource Constraints[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2432-2442. doi: 10.11999/JEIT220692

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Adaptive Optimization Method for Controller Area Network Anomaly Detection under Vehicle Resource Constraints

doi: 10.11999/JEIT220692

ZHANG Jinfeng^{1, 2
,
,},
ZHANG Zhen^{2, 3},
LIU Shaoxun²,
WU Jiangxing^{1, 2, 3}

1.
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
2.
Network Communication and Security Purple Mountain Laboratory, Nanjing 211111, China
3.
National Digital Switching System Engineering and Technological Research and Development Center, Zhengzhou 450002, China

Funds: The Major Science and Technology Project of Henan Province (221100240100), The Major Science and Technology Innovation Special Project of Zhengzhou (2021KJZX0060-3)

Received Date: 2022-05-30
Rev Recd Date: 2022-09-09

Available Online: 2022-09-15

Publish Date: 2023-07-10

Abstract

Abstract

Considering the problem of how to take into account the accuracy and timeliness of Controller Area Network(CAN) anomaly detection under the constraints of limited vehicle resources, an adaptive optimization method for CAN anomaly detection is proposed. Firstly, based on information entropy, the quantification index of the accuracy and timeliness of CAN network anomaly detection is established, and the CAN anomaly detection is modeled as a multi-objective optimization problem. Then, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm for solving the multi-objective optimization problem is designed. The Pareto frontier is used as the optimization and adjustment space of the parameters of the CAN anomaly detection model, and a robust control mechanism of the detection model is proposed to meet the needs of different scenarios. Through experimental analysis, the influence of optimization parameters on anomaly detection is deeply analyzed, and it is verified that the proposed method can adapt to the needs of diverse detection scenarios under limited vehicle resources.

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References(26)

References

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