Entropy-Based Classification of Large-Scale Network Traffic Anomalies

This paper presents an entropy-based large-scale network traffic anomaly classification method for the integrated use of the subspace method and the k-means clustering method.And classifying network traffic anomalies is realized in the experimental environment of campus networks.The experimental results show that large-scale traffic anomaly classification based on entropy not only realizes simple and has a small computation quantity,but also has a high classification precision.