Peer to peer traffic identification using support vector machine and bat-inspired optimization algorithm

Nowadays, Peer-to-Peer computing technology (P2P) is widely used on Internet, which has brought great challenges to effective management of the network. As a result, it is very important to recognize P2P applications as to maintain network. In essence, to identify traffic of P2P is a problem belongs to pattern recognition. As one of the optimal classifiers, support vector machine (SVM) has special advantages with avoiding local optimum, overcoming dimension disaster, resolving small samples and high dimension for P2P classification problems. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. Therefore, in the paper the bat algorithm is proposed to seek the optimal parameters for SVM. In the end, experimental results display that the proposed method outperforms SVM optimized by genetic algorithm, particle swarm optimization algorithm, which can effectively improve the accuracy of P2P network traffic identification.