Poster: Opinion Dynamics for Enhancing Trust and Security in Connected Vehicle Networks
Poster: Opinion Dynamics for Enhancing Trust and Security in Connected Vehicle Networks
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海报：增强互联车辆网络信任和安全的意见动态
DOI：
10.1145/3565287.3617977
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Rathore, Heena
中科院分区：
文献类型：
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作者：
Voce, Gianna;Castillo, Marbella;Griffith, Henry;Rathore, Heena
互联车辆(CV)提供了增强的安全功能和改进的交通管理能力，但面临着一个关键问题-由于互联而容易受到恶意攻击。为了解决这个问题，已经开发了信任算法来评估车辆的可信性。然而，在大多数恶意情况下，这些算法可能会失败。本文探索了一种基于DeGroot观点动力学模型的算法，在这样的场景下达成共识，旨在区分善意和恶意的车辆。该模型在运动模型、开源交通模拟器和真实数据集三个不同的数据集上成功识别了高达98%的善意和恶意车辆，平均F1得分为0.96。
Connected vehicles (CVs) offer enhanced safety features and improved traffic management capabilities but face a critical concern---vulnerability to malicious attacks due to interconnectivity. To address this, trust algorithms have been developed to assess vehicle trustworthiness. However, in majority-malicious conditions, these algorithms may fail. This paper explores an algorithm based on the DeGroot opinion dynamics model to achieve consensus in such scenarios, aiming to distinguish between benevolent and malicious vehicles. The model successfully identified benevolent and malicious vehicles in up to 98% corruption with an average F1 score of 0.96 on three different datasets namely motion model, open source traffic simulator, and real-world dataset.
DOI：
10.1109/icce56470.2023.10043181
发表时间：
2023-01
期刊：
2023 IEEE International Conference on Consumer Electronics (ICCE)
影响因子：
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作者：
Henry Griffith;Malik Farooq;Heena Rathore
通讯作者：
Henry Griffith;Malik Farooq;Heena Rathore
在车辆网络中使用贝叶斯统计过滤器的声誉系统
DOI：
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发表时间：
2020
期刊：
2020 Sixth International Conference on Mobile And Secure Services (MobiSecServ)
影响因子：
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作者：
Y. Begriche;Rida Khatoun;A. Rachini;L. Khoukhi
通讯作者：
L. Khoukhi