CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles
Abstract Connected and automated vehicles (CAVs) are recognized as a promising solution to improve road safety. Before deploying CAVs, securing the communication environment is a critical challenge. To secure the exchanged sensitive messages between CAVs, a novel cooperative trust‐aware tolerant mis...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-02-01
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| Series: | IET Intelligent Transport Systems |
| Online Access: | https://doi.org/10.1049/itr2.12139 |
| _version_ | 1798021125243928576 |
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| author | Ying Liu Hongwei Xue Weichao Zhuang Fa'an Wang Liwei Xu Guodong Yin |
| author_facet | Ying Liu Hongwei Xue Weichao Zhuang Fa'an Wang Liwei Xu Guodong Yin |
| author_sort | Ying Liu |
| collection | DOAJ |
| description | Abstract Connected and automated vehicles (CAVs) are recognized as a promising solution to improve road safety. Before deploying CAVs, securing the communication environment is a critical challenge. To secure the exchanged sensitive messages between CAVs, a novel cooperative trust‐aware tolerant misbehaviour detection system (CT2‐MDS) is proposed in this paper. The vehicle‐to‐everything (V2X) network model in real complex scenarios is built first, and several typical types of attacks are simulated. The cooperative trust factor is then defined to evaluate the received message packet's plausibility instead of the node. And the measuring uncertainty of sensors is considered to improve the overall detection precision. At last, a multi‐model fusion misbehaviour detection method is presented based on the idea of cross‐validation. In this mechanism, different from the binary classification, the misbehaviour messages are categorized into particular misbehaving classes. To verify the proposed system's performance, the simulation is conducted, and the results showed that the proposed method outperformed existing single classifiers and provided good prediction accuracy in different attack density. |
| first_indexed | 2024-04-11T17:08:58Z |
| format | Article |
| id | doaj.art-42486afb08f7482d88b42e1c83c6fd5e |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-11T17:08:58Z |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-42486afb08f7482d88b42e1c83c6fd5e2022-12-22T04:12:58ZengWileyIET Intelligent Transport Systems1751-956X1751-95782022-02-0116221823110.1049/itr2.12139CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehiclesYing Liu0Hongwei Xue1Weichao Zhuang2Fa'an Wang3Liwei Xu4Guodong Yin5School of Cyber Science and Engineering Southeast University Nanjing 211189 ChinaSchool of Cyber Science and Engineering Southeast University Nanjing 211189 ChinaSchool of Mechanical Engineering Southeast University Nanjing 211189 ChinaSchool of Mechanical Engineering Southeast University Nanjing 211189 ChinaSchool of Mechanical Engineering Southeast University Nanjing 211189 ChinaSchool of Mechanical Engineering Southeast University Nanjing 211189 ChinaAbstract Connected and automated vehicles (CAVs) are recognized as a promising solution to improve road safety. Before deploying CAVs, securing the communication environment is a critical challenge. To secure the exchanged sensitive messages between CAVs, a novel cooperative trust‐aware tolerant misbehaviour detection system (CT2‐MDS) is proposed in this paper. The vehicle‐to‐everything (V2X) network model in real complex scenarios is built first, and several typical types of attacks are simulated. The cooperative trust factor is then defined to evaluate the received message packet's plausibility instead of the node. And the measuring uncertainty of sensors is considered to improve the overall detection precision. At last, a multi‐model fusion misbehaviour detection method is presented based on the idea of cross‐validation. In this mechanism, different from the binary classification, the misbehaviour messages are categorized into particular misbehaving classes. To verify the proposed system's performance, the simulation is conducted, and the results showed that the proposed method outperformed existing single classifiers and provided good prediction accuracy in different attack density.https://doi.org/10.1049/itr2.12139 |
| spellingShingle | Ying Liu Hongwei Xue Weichao Zhuang Fa'an Wang Liwei Xu Guodong Yin CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles IET Intelligent Transport Systems |
| title | CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles |
| title_full | CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles |
| title_fullStr | CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles |
| title_full_unstemmed | CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles |
| title_short | CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles |
| title_sort | ct2 mds cooperative trust aware tolerant misbehaviour detection system for connected and automated vehicles |
| url | https://doi.org/10.1049/itr2.12139 |
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