用机器学习检测自动驾驶汽车异常行为，提升车联网安全。
Cybersecurity-Focused Anomaly Detection in Connected Autonomous Vehicles Using Machine Learning
- 构建时序数据集模拟车辆正常与异常交互行为。
- 堆叠LSTM和随机森林模型，预测轨迹并识别异常，精度高。
- 适合关注智能交通与网络安全的研究者阅读。
连接式自动驾驶汽车（CAVs）的异常检测对保障交通网络的安全可靠至关重要，因其易受传感器故障、网络攻击及环境突变影响。本研究通过模拟车辆行为生成数据集，涵盖多辆联网自动驾驶汽车的位置、速度和加速度时序数据。采用堆叠长短期记忆（LSTM）模型捕捉时间依赖性与序列异常，学习标准驾驶模式；同时部署随机森林模型进行集成预测，提升可解释性与性能。随机森林模型实现R2=0.9830，MAE=5.746，95%分位数异常阈值为14.18；堆叠LSTM模型达R2=0.9998，MAE=82.425，95%分位数异常阈值为265.63。结果表明，两模型均能准确预测车辆轨迹并在自动驾驶场景中有效检测异常。
原文摘要 · Abstract (English)
Anomaly detection in connected autonomous vehicles (CAVs) is crucial for maintaining safe and reliable transportation networks, as CAVs can be susceptible to sensor malfunctions, cyber-attacks, and unexpected environmental disruptions. This study explores an anomaly detection approach by simulating vehicle behavior, generating a dataset that represents typical and atypical vehicular interactions. The dataset includes time-series data of position, speed, and acceleration for multiple connected autonomous vehicles. We utilized machine learning models to effectively identify abnormal driving patterns. First, we applied a stacked Long Short-Term Memory (LSTM) model to capture temporal dependencies and sequence-based anomalies. The stacked LSTM model processed the sequential data to learn standard driving behaviors. Additionally, we deployed a Random Forest model to support anomaly detection by offering ensemble-based predictions, which enhanced model interpretability and performance. The Random Forest model achieved an R2 of 0.9830, MAE of 5.746, and a 95th percentile anomaly threshold of 14.18, while the stacked LSTM model attained an R2 of 0.9998, MAE of 82.425, and a 95th percentile anomaly threshold of 265.63. These results demonstrate the models' effectiveness in accurately predicting vehicle trajectories and detecting anomalies in autonomous driving scenarios.
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