P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm

Abstract

Abstract. As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

“…the optimal values of the parameters of CS governing the rate of convergence of the algorithm were obtained through PSO which was shown to guarantee faster learning rate of neural networks with enhanced classification accuracy. Ye et al [123] incorporated CS with PSO in the optimization of Support Vector Machine (SVM) parameters used for classification and identification of peer-to-peer traffic. At the beginning of each iteration, the optimal positions generated by PSO serve as the initial positions for CS and the position vectors of CS-PSO are considered as the pair of candidate parameters of SVM.…”

“…Hybrid PSO CS Compressed image classification Ye et al [123] 2015 Hybrid CSA with PSO Optimization of parameters of SVM Li and Yin [124] 2015 PSCS Global optimization Chen et al [125] 2015 PSOCS Artificial Neural Networks Guo et al [126] 2016 PSOCS Preventive maintenance period optimization model Chi et al [127] 2017 CSPSO Optimization problems Dash et al [128] 2017 ICSPSO Linear phase multi-band stop filters 4.8.6. Hybridization of PSO using Artificial Bee Colony (ABC)…”