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Conference Papers Year : 2020

Queue Length Estimation Based Defence Against Data Poisoning Attack for Traffic Signal Control

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Endong Tong
  • Function : Author
  • PersonId : 1118819
Wenjia Niu
  • Function : Author
  • PersonId : 1051602

Abstract

With the development of intelligent transportation systems, especially in the context of the comprehensive development and popularization of big data and 5G networks, intelligent transportation signal systems have been experimented and promoted in various countries around the world. As with other big data-based systems, specific attacks pose a threat to the security of big data-based intelligent transportation system systems. Targeting system vulnerabilities, certain simple forms of attack will have a huge impact on signal planning, making Actual traffic is congested. In this article, we first show a specific attack and then add more attack points, analyze the system’s vulnerabilities, and model based on traffic waves and Bayesian predictions, so that the attack points can help the impact is weakened and the traffic can function normally. For experiments, we performed traffic simulation on the VISSIM platform to prove the impact of our attack and further verify the accuracy and effectiveness of the model.
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Dates and versions

hal-03456969 , version 1 (30-11-2021)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Xu Gao, Jiqiang Liu, Yike Li, Xiaojin Wang, Yingxiao Xiang, et al.. Queue Length Estimation Based Defence Against Data Poisoning Attack for Traffic Signal Control. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.254-265, ⟨10.1007/978-3-030-46931-3_24⟩. ⟨hal-03456969⟩
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