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

• URL: http://arxiv.org/abs/2503.16047v2
• Date: Fri, 21 Mar 2025 17:40:15 GMT
• Title: Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic
• Authors: Bisola Faith Kayode, Akinyemi Sadeeq Akintola, Oluwole Fagbohun, Egonna Anaesiuba-Bristol, Onyekachukwu Ojumah, Oluwagbade Odimayo, Toyese Oloyede, Aniema Inyang, Teslim Kazeem, Habeeb Alli, Udodirim Ibem Offia, Prisca Chinazor Amajuoyi,
• Abstract summary: We propose a novel Temporal-Spatial Attention Network (TSAN) architecture for detecting Denial of Service (DoS) attacks in network traffic.<n>By leveraging both temporal and spatial features of network traffic, our approach captures complex traffic patterns and anomalies that traditional methods might miss.<n> Experimental results on the NSL-KDD dataset demonstrate that TSAN outperforms state-of-the-art models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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