Entropy-Based Heavy Tailed Distribution Transformation for Network Traffic Analysis

Abstract

In general, network traffic data has a heavy-tailed probability distribution. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) has been developed to convert the heavy tailed network traffic data distribution into a transformed probability distribution. In practice, the entropy distribution of the transformed probability distribution exhibits a type of linearity that gives rise to an eigenstructure that allows the characterization of network traffic data to effectively lossily compress network traffic data via the Rate Controlled Eigen-Based Coding. The aforementioned eigenstructure is motivated by singular value decomposition theory. A very high compression ratio can be achieved by the proposed method. Results of applying the methods to real network traffic data network traffic data are presented.