An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus
This provides a robust solution for real-world CAN security challenges in intelligent transportation systems, though it appears incremental over existing methods.
The study tackled the problem of detecting anomalies in CAN bus systems for intelligent transportation security by proposing a cascade feature-level spatiotemporal fusion framework, achieving an AUC-ROC of 0.9987 and 100% accuracy on the CAR-HACKING dataset.
Intelligent transportation systems (ITS) play a pivotal role in modern infrastructure but face security risks due to the broadcast-based nature of the in-vehicle Controller Area Network (CAN) buses. While numerous machine learning models and strategies have been proposed to detect CAN anomalies, existing approaches lack robustness evaluations and fail to comprehensively detect attacks due to shifting their focus on a subset of dominant structures of anomalies. To overcome these limitations, the current study proposes a cascade feature-level spatiotemporal fusion framework that integrates the spatial features and temporal features through a two-parameter genetic algorithm (2P-GA)-optimized cascade architecture to cover all dominant structures of anomalies. Extensive paired t-test analysis confirms that the model achieves an AUC-ROC of 0.9987, demonstrating robust anomaly detection capabilities. The Spatial Module improves the precision by approximately 4%, while the Temporal Module compensates for recall losses, ensuring high true positive rates. The proposed framework detects all attack types with 100% accuracy on the CAR-HACKING dataset, outperforming state-of-the-art methods. This study provides a validated, robust solution for real-world CAN security challenges.