Multi-Sensors Space and Time Dimension Based Intrusion Detection System in Automated Vehicles

Recent developments in intelligence technologies have led to an explosion in the use of connected and automated vehicles (CAVs). Unfortunately, these autonomous vehicles face an increasing risk of vulnerability due to various attacks. Vehicle intrusion detection mechanisms are widely employed to mitigate the threats. Although some works have addressed this issue, few works consider situations that the attack detection may be fooled via simultaneous attacking multiple sensors. In this article, we propose a novel intrusion detection system integrated Space Dimension Model and Time Dimension Model based on sensor data fusion for countering both independent attack and confederate attack in automated vehicles. In Space Dimension Model, the correlations of multivariate in-vehicle sensor data among multiple sensors are utilized as the input of our optimized convolutional neural network (CNN) model. Especially, we construct vehicle state matrices to characterize the underlying correlation of data between each sensor and other sensors, and then input them into our network for classification. To describe the abrupt deviation caused by anomalous multivariate sensor data itself, we design Time Dimension Model to capture the sensor behaviors of vehicle state vectors at adjacent time by utilizing the Mahalanobis distance (MD) metric. Extensive empirical studies based on a real-world vehicular dataset demonstrate the effectiveness of our integrated anomaly detection mechanisms by a comparative analysis with two detection models that consider only Space Dimension and Time Dimension respectively. The method can outperform the related work under different scenarios 1) with the gain of up to 3.01% in accuracy and 3.04% in F1 score; 2) with ability of defending against confederate attack effectively.