A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

Comparing P2PTV Traffic Classifiers

Peer-to-Peer IP Television (P2PTV) applications represent one of the fastest growing application classes on the Internet, both in terms of their popularity and in terms of the amount of traffic they generate. While network operators require monitoring tools that can effectively analyze the traffic produced by these systems, few techniques have been tested on these mostly closed-source, proprietary applications. In this paper we examine the properties of three traffic classifiers applied to the problem of identifying P2PTV traffic. We report on extensive experiments conducted on traffic traces with reliable ground truth information, highlighting the benefits and shortcomings of each approach. The results show that not only their performance in terms of accuracy can vary significantly, but also that their usability features suggest different effective aspects that can be integrate