Privacy Preservation for Network Traffic Classification

Abstract

With the rapid development of Internet technology and massive demands of data sharing, data privacy issues have attracted more and more attention in recent years. The paper analyzes the network traffic classification methods and designs the features subset selection algorithm based on information entropy. The proposed privacy preserving algorithm is based on data perturbation. By applying the algorithm on the real network traffic data set, it is shown that the network traffic data protected by the algorithm can effectively ensure data security while maintaining data utility, which contributes to balance the contradiction between them in existing algorithms. It effectively solves the privacy leakage problem of network traffic in the process of data mining.