Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic
This addresses network security threats for organizations by improving detection of evolving DoS attack patterns, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of detecting Denial-of-Service (DoS) attacks in network traffic by proposing a Temporal-Spatial Attention Network (TSAN), which achieves superior accuracy, precision, recall, and F1-score on the NSL-KDD dataset compared to state-of-the-art models.
Denial-of-Service (DoS) attacks remain a critical threat to network security, disrupting services and causing significant economic losses. Traditional detection methods, including statistical and rule-based models, struggle to adapt to evolving attack patterns. To address this challenge, we propose 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 TSAN outperforms state-of-the-art models, achieving superior accuracy, precision, recall, and F1-score while maintaining computational efficiency for real-time deployment. The proposed architecture offers an optimal balance between detection accuracy and computational overhead, making it highly suitable for real-world network security applications.