A data-driven approach shows improved robustness against cyber attacks in intelligent transportation, indicating a need for advanced intrusion detection systems.
Key Points
Our model significantly enhances robustness against cyber attacks using convolutional neural networks and attention mechanisms.
It integrates advanced features for spatio-temporal pattern analysis and improves intrusion detection systems.
The framework processes multi-modal data effectively while maintaining real-time performance for intelligent transportation.
Extensive experiments demonstrate superior detection accuracy compared to existing AI-based detection methods.