Application Based Network Traffic Dataset and SPID Analysis

Abstract

Currently, web-based applications have become a part of every piece of our daily lives. The rapid advancements in these applications which have found its use in variety of sectors has made it necessary for the respective security systems to adapt as fast in order to identify these applications. In this work, some up-to-date and commonly used applications’ web traffic data have been collected for network traffic classification problem and they are presented for the use of researchers. In addition, an analysis of these data with respect to this problem is performed using features based on statistical protocol identification. It has been shown that for the traffic classification, training a Random Forest classifier with these features is more effective than using the mean KL divergence which was used in previous work with these features.