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Advancing connected vehicle security through real-time sensor anomaly detection and recovery
Vehicular Communications ( IF 5.8 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.vehcom.2025.100876
Akshit Singh, Heena Rathore

Connected Vehicles (CVs) are a crucial element in the evolution of smart transportation systems, utilizing communication and sensing technologies to interact with each other and with infrastructure. As these vehicles become more interconnected, the risk of their components being affected by anomalies or intentional malicious attacks grows. It is essential, therefore, to identify and filter out any anomalous data to ensure reliable decision-making. Existing solutions for anomaly detection in CVs include methods such as kalman filter, cumulative summation, convolutional neural networks and other machine learning models. However, a prevalent issue is the limited universality of anomaly datasets along with the variability introduced by simulated data. Additionally, there are few methods for recovering the network from anomalies using sensor information. In this paper, we address these limitations by utilizing the Tampa CV (TCV) dataset and incorporating anomalies such as bias, noise, and spikes. Furthermore, we present a novel method for real-time anomaly detection in CVs using Bayesian Online Change Point Detection (BOCPD). We propose a unique recovery mechanism that employs Bayesian forecasting to interpret identified anomalies, marking the first of its kind in this field. This approach significantly enhances the security of CV systems by seamlessly merging instant detection with swift recovery, ensuring continuous protection against data integrity threats. Results demonstrate that the proposed model achieves an average accuracy improvement of 53.83 % over other machine learning models. This paper makes advancement through real-time anomaly detection and recovery mechanisms, thus significantly improving the resilience of smart transportation systems against data integrity threats.

中文翻译:


通过实时传感器异常检测和恢复来提高互联汽车的安全性



互联汽车 (CV) 是智能交通系统发展的关键要素,它利用通信和传感技术相互交互以及与基础设施交互。随着这些车辆的互联程度越来越高,其组件受到异常或故意恶意攻击影响的风险也会增加。因此,必须识别并过滤掉任何异常数据,以确保做出可靠的决策。现有的 CV 异常检测解决方案包括 kalman filter、累积求和、卷积神经网络和其他机器学习模型等方法。然而,一个普遍的问题是异常数据集的有限通用性以及模拟数据引入的可变性。此外,使用传感器信息从异常中恢复网络的方法很少。在本文中,我们利用坦帕 CV (TCV) 数据集并结合偏差、噪声和尖峰等异常情况来解决这些限制。此外,我们提出了一种使用贝叶斯在线变化点检测 (BOCPD) 对 CV 进行实时异常检测的新方法。我们提出了一种独特的恢复机制,该机制采用贝叶斯预测来解释已识别的异常,这在该领域尚属首创。这种方法将即时检测与快速恢复无缝结合,确保持续保护免受数据完整性威胁,从而显著提高 CV 系统的安全性。结果表明,与其他机器学习模型相比,所提出的模型实现了 53.83% 的平均准确率提升。 本文通过实时异常检测和恢复机制取得了进展,从而显著提高了智能交通系统对数据完整性威胁的弹性。
更新日期:2025-01-18
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