Detection and Management of P2P Traffic in Networks using Artificial Neural Networksa

• Published in: Journal of network and systems management Vol. 30; no. 2
• Main Authors: Mills, Godfrey A., Pomary, Pamela, Togo, Emmanuel, Sowah, Robert A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Artificial neural networks; Bandwidths; Classifiers; Colleges & universities; Communications Engineering; Computer Communication Networks; Computer Science; Computer Systems Organization and Communication Networks; Information Systems and Communication Service; Machine learning; Monitoring; Multilayer perceptrons; Multimedia; Networks; Operations Research/Decision Theory; Peer to peer computing; Real time operation; Self organizing maps; Traffic congestion; Traffic control; Traffic flow; Traffic information; Traffic management; P2P traffic detection; Self-organizing map; P2P traffic management; Machine-learning; Multilayer perceptron network
• ISSN: 1064-7570; 1573-7705
• DOI: 10.1007/s10922-021-09637-1

Peer-to-Peer (P2P) technology is a popular tool for sharing files and multimedia services on networks. While the technology has been serving a good purpose of facilitating sharing of large volumes of data on networks, in other aspects, it has also become a potential source through which attackers could ride on to launch various malicious attacks on the networks. In networks with limited bandwidth resources, uncontrolled P2P activities may also come with problems of congestion in such networks. As P2P continues to evolve on the internet in more complex forms, the need for dynamic mechanisms with the ability to learn the evolving P2P behavior will be essential for accurate monitoring and detection of the P2P traffic to minimize its effects on networks. Supervised machine learning classifiers have been used in recent times, as potential tools for monitoring and detection of the P2P traffic. Incidentally, the capabilities of such classifiers decline over time due to the changing dynamics of the P2P features, making it necessary for the classifiers to undergo continuous retraining in order to maintain their capability of providing effective detection of new P2P traffic features in real-time operations. This paper presents a hybrid machine-learning framework that combines the capabilities of self-organizing map (SOM) model with a multilayer perceptron (MLP) network to achieve real-time detection of P2P traffic in networks. The SOM model generates sets of clustered features contained in the traffic flows and organizes the features into P2P and non-P2P, which are used for training the MLP model for subsequent detection and control of the P2P traffic. The proposed P2P detection framework was tested using real traffic data from the University of Ghana campus network. The test results revealed an average detection rate of 99.89% of the observed instances of P2P traffic in the experimental data. The good detection rate from the detection framework suggests its capability to serve as a potential tool for dynamic monitoring, detection, and control of P2P traffic to manage bandwidth resources and isolation of undesirable P2P-driven traffic in networks.