Abstract: Network attackers use multiple nodes such as dark net,stepping stones,and other relay links to create complex and unpredictable attack path,making it difficult to trace the entire chain,leading to unstable detection effectiveness of large-scale network.Therefore,a large-scale network defense algorithm based on temporal network pipeline detection technology is proposed.This algorithm uses time intervals to group large-scale network data streams,reducing false alarms caused by single parameter anomalies.By using convolutional encoding and traffic modulation,the temporal watermark embedding of the data stream is achieved,so that the watermark information can maintain a certain stability in the face of network traffic fluctuations,enhancing the robustness of the watermark.By comparing the joint centroid entropy of multiple streams in a temporal network,the marked streams containing watermarks could be identified quickly.Experiment shows that the proposed algorithm is less affected by jitter and can ensure watermark embedding,achieving defense against large-scale network attacks.

Key words: Cyber security, Flow detection, Temporal, Large-scale network defense, Network stream watermarking, Joint centroid entropy