Data-driven-based spatio-temporal detection model against cyber attacks in Intelligent transportation

The escalating sophistication of cyber attacks in intelligent transportation systems necessitates the development of advanced intrusion detection systems capable of processing multi-modal data efficiently while maintaining real-time performance. To address these critical cybersecurity challenges, this paper presents a novel spatio-temporal hybrid model for robust attack detection. Our framework, designed to overcome the limitations of existing methods in handling complex hybrid threats, integrates three key components: convolutional neural networks (CNN) for spatial feature extraction; bidirectional long short-term memory (BiLSTM) networks to capture long-range temporal dependencies, and an attention mechanism for adaptive feature weighting. This innovative architecture enables comprehensive spatio-temporal pattern analysis while dynamically prioritizing the most discriminative features. Extensive experimental results demonstrate that our model significantly outperforms existing AI-based detection methods, such as Korea New Network-KNN and Multilayer Perceptron-MLP, achieving superior detection accuracy and enhanced robustness against sophisticated attacks.