Flow-Based P2P Network Traffic Classification Using Machine Learning

With the introduction of new and new services in the market every day, the internet is growing rapidly. The network traffic generated by these network protocols and applications needs to be categorised which is an important task of network management. Among these, p2p has the largest share of the bandwidth. This great demand in the bandwidth has increased the importance of network traffic engineering. So, in order to meet the current demand and develop new architectures which help in improving the network performance, a broad understanding of the network traffic properties is required. The flow based methods classify p2p and non-p2p traffic using the characteristics of flows on the internet. In this paper, Naïve Bayes estimator is used to categorize the traffic into p2p and non-p2p. Our results show that with the right set of features and good training data, high level of accuracy is achievable with the simplest of Naïve Bayes estimator.