Classification of audio and video traffic over HTTP protocol

A widely use of the Internet introduces various online services such as online game, radio online, music online, TV online and video clips, which communicate over hyper text transfer protocol (HTTP). In this work, we aim to classify an audio and a video traffic from normal Web traffic to reduce the misuse of bandwidth consumption. We propose a classification method based on flow information. Our classification use a combination of keyword matching technique and statistical behavior profiles. Keywords are pre-defined by observing from both audio and video traffic. Behavior profiles consist of three attributes, which are the average received packet size, a ratio of number of server-client packets, and the flow duration. Each attribute have an independent threshold of mean (mu) and standard deviation (sigma). The experimental results show that our method can classify an audio traffic, a video traffic and normal Web traffic with a high precision and recall.