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<p>Connected and Autonomous Vehicle (CAV) technology is rising in the transport sector due to its enormous benefits, including better traffic safety and efficiency. The intelligent decision in CAV relies on traffic data from in-vehicle sensors, neighbouring vehicles, and roadside infrastructure. The traffic data is prone to manipulation by internal and external attacks. This NTU’s CAV research focuses on message manipulation attacks, such as false position attacks. In this context, this paper proposes a novel misbehaviour prediction framework to identify malicious positions in CAV networks based on an aggregated trust analysis strategy. The proposed framework utilises Mamdani fuzzy logic for identification and will be applied to the onboard unit of every vehicle without depending on external infrastructure for validation. Through repeated observation of the concerned CAVs’ behaviour and trust values aggregation, the framework can predict potential misbehaviour accurately before it leads to safety-critical issues. The framework is validated using the <i>VeReMi</i> extension dataset and tested with supervised machine learning algorithms, including Decision Tree (DT), k-nearest Neighbour (KNN), Logistic Regression (LR), and Support Vector Machine (SVM). The simulation results show that the DT and SVM yield the highest sensitivity in detecting false positions. Although we used the results with KNN, the default module in previous studies, we discovered that it was competitively effective in identifying all position attacks, doing very well with eventual stop position attacks. Additional examination of the trust aggregation fuzzy model indicates that the F1 measure for constant false position attacks, random position attacks, and eventual stop position attacks is above 0.99 on average. In contrast, the F1 score for random and fixed offsets is less than 0.70. To achieve improved performance, we use weights that are inversely proportional to the sender-receiver distance for post-processing. We also analyse the performance of our proposed model against other fuzzy logic models. Overall, our fuzzy model shows competitive performance in detecting false position data, as indicated by the performance values.</p>

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Misbehaviour Prediction in CAV Network using Aggregated Trust Analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月30日

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  • Maria Drolence Mwanje,
  • Omprakash Kaiwartya,
  • Devki Nandan Jha,
  • Ahmad M. Khasawneh,
  • Ali Sadiq,
  • Yue Cao

摘要

Connected and Autonomous Vehicle (CAV) technology is rising in the transport sector due to its enormous benefits, including better traffic safety and efficiency. The intelligent decision in CAV relies on traffic data from in-vehicle sensors, neighbouring vehicles, and roadside infrastructure. The traffic data is prone to manipulation by internal and external attacks. This NTU’s CAV research focuses on message manipulation attacks, such as false position attacks. In this context, this paper proposes a novel misbehaviour prediction framework to identify malicious positions in CAV networks based on an aggregated trust analysis strategy. The proposed framework utilises Mamdani fuzzy logic for identification and will be applied to the onboard unit of every vehicle without depending on external infrastructure for validation. Through repeated observation of the concerned CAVs’ behaviour and trust values aggregation, the framework can predict potential misbehaviour accurately before it leads to safety-critical issues. The framework is validated using the VeReMi extension dataset and tested with supervised machine learning algorithms, including Decision Tree (DT), k-nearest Neighbour (KNN), Logistic Regression (LR), and Support Vector Machine (SVM). The simulation results show that the DT and SVM yield the highest sensitivity in detecting false positions. Although we used the results with KNN, the default module in previous studies, we discovered that it was competitively effective in identifying all position attacks, doing very well with eventual stop position attacks. Additional examination of the trust aggregation fuzzy model indicates that the F1 measure for constant false position attacks, random position attacks, and eventual stop position attacks is above 0.99 on average. In contrast, the F1 score for random and fixed offsets is less than 0.70. To achieve improved performance, we use weights that are inversely proportional to the sender-receiver distance for post-processing. We also analyse the performance of our proposed model against other fuzzy logic models. Overall, our fuzzy model shows competitive performance in detecting false position data, as indicated by the performance values.