SDN Anomalous Traffic Detection Based on Temporal Convolutional Network

The wide application of software-defined network (SDN) architecture, combined with its centralized control characteristics, have exacerbated the potential risk of network attacks, and the traditional anomaly traffic detection methods are facing the challenges of high false alarm rate and insufficient generalization ability due to the reliance on manual rule design and the difficulty in capturing dynamic temporal features. In response to these challenges, we propose a Temporal Convolutional Network (TCN)-based anomalous traffic detection method for SDN. Taking the packet length sequence as the core feature, the long-term temporal dependency in the traffic data is effectively captured by causal convolution and dilation convolution operations of the TCN model, combined with the residual connection mechanism to optimize the gradient propagation and improve the stability of the model training. The experiments validate the model performance based on the public InSDN dataset, and the results show that the method achieves high accuracy in the binary classification task of normal and malicious traffic and improves its detection accuracy by about 5% compared with traditional statistical methods and mainstream deep learning models.