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

Peer-to-peer (P2P) applications have been popular among users for more than a decade. They consume a lot of network bandwidth, due to the fact that network administrators face several issues such as congestion, security, managing resources, etc. Hence, its accurate classification will allow them to maintain a Quality of Service for various applications. Conventional classification techniques, i.e., port-based and payload-based techniques alone, have proved ineffective in accurately classifying P2P traffic as they possess significant limitations. As new P2P applications keep emerging and existing applications change their communication patterns, a single classification approach may not be sufficient to classify P2P traffic with high accuracy. Therefore, a multi-level P2P traffic classification technique is proposed in this paper, which utilizes the benefits of both heuristic and statistical-based techniques. By analyzing the behavior of various P2P applications, some heuristic rules have been proposed to classify P2P traffic. The traffic which remains unclassified as P2P undergoes further analysis, where statistical-features of traffic are used with the C4.5 decision tree for P2P classification. The proposed technique classifies P2P traffic with high accuracy (i.e., 98.30%), works with both TCP and UDP traffic, and is not affected even if the traffic is encrypted.

Classifying Internet Traffic using an Efficient Classifier

The new development in the architecture of Internet has increased internet traffic. The introduction of Peer to Peer (P2P) applications are affecting the performance of traditional internet applications. Network optimization is used to monitor and manage the internet traffic and improve the performance of internet applications. The existing optimizations methods are not able to provide better management for networks. Machine learning (ML) is one of the familiar techniques to handle the internet traffic. It is used to identify and reduce the traffic. The lack of relevant datasets have reduced the performance of ML techniques in classification of internet traffic. The aim of the research is to develop a hybrid classifier to classify the internet traffic data and mitigate the traffic. The proposed method is deployed in the classification of traffic traces of University Technology Malaysia. The method has produced an accuracy of 98.3% with less computation time

A Survey over Mechanisms for Reducing Free Riding Behaviour in Structured P2P Networks

P2P (peer-to-peer) overlays have attracted many researchers due to increase in Internet based P2P applications. A P2P overlay is a distributed system in which the independent nodes participate at their will to share resources in distributed manner. P2P overlays are designed for wired based communication systems but today P2P applications are becoming popular in wireless networks even in multi-hop wireless networks. Routing mechanism in P2P overlays is based on IP infrastructure and many protocols are deployed successfully for efficient and fair P2P applications. However, there are many challenges in designing and deployment of efficient and fair protocols for the P2P overlays. These overlays suffer from many challenges such as dynamic overlay management, lack of robust trust model, counterfeit content distribution, free riding, poor resource search scalability, security, etc. The churn rate of nodes (join and leave of nodes) in the P2P overlay makes overlay management and resource searching more challenging. The free riding nature of the nodes in a P2P overlay is undesirable and it creates unfairness in the P2P overlay. There are several mechanisms proposed such as Eigen Trust model, tit-for-tat policy, point-based incentive policy, Page Rank policy, layered taxation, advertisement of incentive, etc. for encouraging fairness in the P2P overlays. I have presented in-depth survey over free riding behaviour, its effect, and existing mechanisms to reduce free riding in structured P2P overlays.

The Two-Step P2P Simulation Approach

In this article a framework is introduced that can be used to analyse the effects & requirements of P2P applications onapplication and on network layer. P2P applications are complex and deployed on a large scale, pure packet level simulations do not scale well enough to analyse P2P applications in a large network with thousands of peers. It is also difficult to assess the effect of application level behavior on the communication system. We therefore propose an approach starting with a more abstract and therefore scalable application level simulation. For the application layer a specific simulation framework wasdeveloped. The results of the application layer simulations plus some estimated background traffic are fed into a packet layer simulator like NS2 (or our lab testbed) in a second step to perform some detailed packet layer analysis such as loss and delay measurements. This can be done for a subnetwork of the original network to avoid scalability problems.

ONLINE PEER-TO-PEER TRAFFIC IDENTIFICATION BASED ON COMPLEX EVENTS PROCESSING OF TRAFFIC EVENT SIGNATURES

Peer-to-Peer (P2P) applications are bandwidth-heavy and lead to network congestion. The masquerading nature of P2P traffic makes conventional methods of its identification futile. In order to manage and control P2P traffic efficiently preferably in the network, it is necessary to identify such traffic online and accurately. This paper proposes a technique for online P2P identification based on traffic events signatures. The experimental results show that it is able to identify P2P traffic on the fly with an accuracy of 97.7%, precision of 98% and recall of 99.2%.