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A Many-Objective Anomaly Detection Model for Vehicle Network Based on Federated Learning and Differential Privacy Protection

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Exploration of Novel Intelligent Optimization Algorithms (ISICA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1590))

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Abstract

With the rapid development of Internet of things, vehicle network has become the focus of smart city construction. However, due to the lack of encryption and authentication mechanism, the vehicle network is vulnerable to malicious attacks and intrusion, which poses a threat to the driver’s life safety and data privacy. To solve this problem, this paper proposes a method combining federal learning and differential privacy protection to protect the privacy of drivers. A many-objective anomaly detection model based on Federated learning is constructed. The model optimizes the accuracy, loss, privacy protection degree and communication cost in the federated training model at the same time. The model can obtain better accuracy and privacy protection with less loss and communication cost. The model is solved by many-objective evolutionary algorithm, and some solutions are selected as training parameters, and satisfactory results are obtained. The effectiveness of the proposed method is verified.

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Acknowledgement

This work was supported by Key R&D program of Shanxi Province (International Cooperation) under Grant No. 201903D421048, Project of Shanxi Province under Grant No. 2021Y696.

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Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China

    Tian Fan, Zhixia Zhang, Yang Lan & Zhihua Cui

Authors
  1. Tian Fan
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  2. Zhixia Zhang
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  3. Yang Lan
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  4. Zhihua Cui
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Corresponding author

Correspondence to Zhihua Cui .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. The University of Aizu, Aizu-Wakamatsu, Japan

    Yong Liu

  3. South China Agricultural University, Guangzhou, China

    Wenxiang Wang

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Fan, T., Zhang, Z., Lan, Y., Cui, Z. (2022). A Many-Objective Anomaly Detection Model for Vehicle Network Based on Federated Learning and Differential Privacy Protection. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_6

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  • DOI: https://doi.org/10.1007/978-981-19-4109-2_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4108-5

  • Online ISBN: 978-981-19-4109-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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