Cross-Layer Peer-to-Peer Track Identification and Optimization Based on Active Networking

P2P applications appear to emerge as ultimate killer applications due to their ability to construct highly dynamic overlay topologies with rapidly-varying and unpredictable traffic dynamics, which can constitute a serious challenge even for significantly over-provisioned IP networks. As a result, ISPs are facing new, severe network management problems that are not guaranteed to be addressed by statically deployed network engineering mechanisms. As a first step to a more complete solution to these problems, this paper proposes a P2P measurement, identification and optimisation architecture, designed to cope with the dynamicity and unpredictability of existing, well-known and future, unknown P2P systems. The purpose of this architecture is to provide to the ISPs an effective and scalable approach to control and optimise the traffic produced by P2P applications in their networks. This can be achieved through a combination of different application and network-level programmable techniques, leading to a crosslayer identification and optimisation process. These techniques can be applied using Active Networking platforms, which are able to quickly and easily deploy architectural components on demand. This flexibility of the optimisation architecture is essential to address the rapid development of new P2P protocols and the variation of known protocols

A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

Mining Unclassified Traffic Using Automatic Clustering Techniques

In this paper we present a fully unsupervised algorithm to identify classes of traffic inside an aggregate. The algorithm leverages on the K-means clustering algorithm, augmented with a mechanism to automatically determine the number of traffic clusters. The signatures used for clustering are statistical representations of the application layer protocols. The proposed technique is extensively tested considering UDP traffic traces collected from operative networks. Performance tests show that it can clusterize the traffic in few tens of pure clusters, achieving an accuracy above 95%. Results are promising and suggest that the proposed approach might effectively be used for automatic traffic monitoring, e.g., to identify the birth of new applications and protocols, or the presence of anomalous or unexpected traffi

Comparing P2PTV Traffic Classifiers

Peer-to-Peer IP Television (P2PTV) applications represent one of the fastest growing application classes on the Internet, both in terms of their popularity and in terms of the amount of traffic they generate. While network operators require monitoring tools that can effectively analyze the traffic produced by these systems, few techniques have been tested on these mostly closed-source, proprietary applications. In this paper we examine the properties of three traffic classifiers applied to the problem of identifying P2PTV traffic. We report on extensive experiments conducted on traffic traces with reliable ground truth information, highlighting the benefits and shortcomings of each approach. The results show that not only their performance in terms of accuracy can vary significantly, but also that their usability features suggest different effective aspects that can be integrate

SecMon: End-to-End Quality and Security Monitoring System

The Voice over Internet Protocol (VoIP) is becoming a more available and popular way of communicating for Internet users. This also applies to Peer-to-Peer (P2P) systems and merging these two have already proven to be successful (e.g. Skype). Even the existing standards of VoIP provide an assurance of security and Quality of Service (QoS), however, these features are usually optional and supported by limited number of implementations. As a result, the lack of mandatory and widely applicable QoS and security guaranties makes the contemporary VoIP systems vulnerable to attacks and network disturbances. In this paper we are facing these issues and propose the SecMon system, which simultaneously provides a lightweight security mechanism and improves quality parameters of the call. SecMon is intended specially for VoIP service over P2P networks and its main advantage is that it provides authentication, data integrity services, adaptive QoS and (D)DoS attack detection. Moreover, the SecMon approach represents a low-bandwidth consumption solution that is transparent to the users and possesses a self-organizing capability. The above-mentioned features are accomplished mainly by utilizing two information hiding techniques: digital audio watermarking and network steganography. These techniques are used to create covert channels that serve as transport channels for lightweight QoS measurement's results. Furthermore, these metrics are aggregated in a reputation system that enables best route path selection in the P2P network. The reputation system helps also to mitigate (D)DoS attacks, maximize performance and increase transmission efficiency in the network.Comment: Paper was presented at 7th international conference IBIZA 2008: On Computer Science - Research And Applications, Poland, Kazimierz Dolny 31.01-2.02 2008; 14 pages, 5 figure