The Comp-TSSs Scheme for Anomaly Detection in AI-Powered Autonomous Driving

Given the vulnerability of vehicular networks to security attacks and the criticality of secure AI-powered autonomous driving, this paper emphasizes the security issue concerning vehicular networks in AI-powered autonomous vehicles. The novel complementary tensor summary statistics named as Comp-TSSs, is proposed for the statistical depiction of discrepancy between normal and abnormal volume instances in vehicular networks. This suggested Comp-TSSs enhances vehicular network security by incorporating reconstruction and regularization statistic terms derived from TPCA, which is extended from PCA through a fresh perspective of fully diagonalizing the covariance tensor. Comp-TSSs effectively captures multi-dimensional correlations in vehicular network volume data, providing complementary measures for representation residuals and weighted distances of instances projected in the principal tensor subspace. Building upon Comp-TSSs, a non-parametric statistic framework is developed for real-time detection of diverse volume anomalies, ensuring the security of AI-powered autonomous driving. The theoretical analyses concerning its detection performance and parameter selection are provided as well. Extensive experiments on synthetic and real-world datasets validate our superior vehicular network security monitoring system for AI-powered autonomous vehicles. It demonstrates higher true positive rates, lower false alarm rates, and minimal detection delays, even when both of the energy and variance anomalies are present.