Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic

This paper presents a novel Temporal-Spatial Attention Network (TSAN) architecture for detecting Denial of Service (DoS) attacks in network traffic. By leveraging both temporal and spatial features of network traffic, our approach captures complex traffic patterns and anomalies that traditional methods might miss. The TSAN model incorporates transformer-based temporal encoding, convolutional spatial encoding, and a cross-attention mechanism to fuse these complementary feature spaces. Additionally, we employ multi-task learning with auxiliary tasks to enhance the model’s robustness. Experimental results on the NSL-KDD dataset demonstrate that our approach achieves superior performance compared to state-of-the-art methods, with high accuracy and AUC scores for DoS attack detection. The proposed architecture offers a balance between detection accuracy and computational efficiency, making it suitable for real-time network security applications