A Preliminary Investigation on the Identification of Peer to Peer Network Applications

Abstract

Identification of P2P (peer to peer) applications inside network traffic plays an important role for route provisioning, traffic policing, flow prioritization, network service pricing, network capacity planning and network resource management. Inspecting and identifying the P2P applications is one of the most important tasks to have a network that runs efficiently. In this paper, we focus on identification of different P2P applications. To this end, we explore four commonly used supervised machine learning algorithms as C4.5, Ripper, SVM(Support Vector Machines), Naïve Bayesian and well known unsupervised machine learning algorithm K-Means on four different datasets. We evaluate their performances to identify the P2P applications that each traffic flow belongs to. Evaluations show that, Ripper algorithm gives better results than the others.

Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach

“…Hence, it would lead to many miss-classifications, due to which a high detection rate may not be achieved. Bozdogan et al [13] assessed four supervised and one un-supervised ML algorithms, namely SVM, C4.5 decision tree, Ripper, Naïve Bayesian, and K-means, respectively, for the identification of P2P network applications. They found that Ripper and C4.5 algorithms have a similar performance with the detection rate ranging between 58.9-99.1% and 15.6-98.1%, respectively.…”

“…goto step 17 (6) else if (flw.fh == true) (7) write: flw → P2P (8) else ( 9) { ( 10) fset = flw.ff (11) rst = flw.MLA (fset) (12) if (rst == "P2P") (13) write: flw → P2P ( 14) else (15) write: flw → non-P2P ( 16) } ( 17) flw = fetch_flow() ( 18) }while (flw ! = NULL) End…”

Identifying P2P traffic: A survey

“…The experiment performed on traffic traces of P2P applications include BitTorrent, eMule, PPTV & Cbox and achieved true positive rate ranging from 95.4 to 98.63 % and false positive rate of 0.01 %. Bozdogan et al [92] evaluated the performance of machine learning algorithms for classification of P2P applications, which include BitCommet, uTorrent and BitTorrent. Four supervised algorithms (C4.5, Ripper, SVM and Naive Bayes) and one un-supervised algorithm (K-means) were evaluated using the metrics: detection rate, false positive rate, f-measure and correctly classification rate.…”