TSAN模型通过时空注意力机制，精准识别网络中的拒绝服务攻击。
Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic
- 融合时序与空间特征的Transformer+卷积架构
- 在NSL-KDD数据集上达到更高检测准确率和召回率
- 适合实时部署，兼顾精度与计算效率
拒绝服务（DoS）攻击仍是网络安 全的重大威胁，导致服务中断和巨大经济损失。传统统计与规则方法难以应对不断演变的攻击模式。为此，我们提出一种新型时空注意力网络（TSAN），用于检测网络流量中的DoS攻击。该模型结合流量的时序与空间特征，捕捉传统方法易忽略的复杂模式与异常。其架构采用基于Transformer的时序编码、卷积空间编码以及交叉注意力机制，融合互补特征空间；同时引入多任务学习的辅助任务以增强鲁棒性。在NSL-KDD数据集上的实验表明，TSAN优于现有最先进模型，在准确率、精确率、召回率和F1分数上均表现更优，且具备实时部署所需的计算效率。该架构在检测精度与计算开销间取得良好平衡，适用于真实网络安防场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。