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

Independent comparison of popular DPI tools for traffic classification

Deep Packet Inspection (DPI) is the state-of-the-art technology for traffic classification. According to the conventional wisdom, DPI is the most accurate classification technique. Consequently, most popular products, either commercial or open-source, rely on some sort of DPI for traffic classification. However, the actual performance of DPI is still unclear to the research community, since the lack of public datasets prevent the comparison and reproducibility of their results. This paper presents a comprehensive comparison of 6 well-known DPI tools, which are commonly used in the traffic classification literature. Our study includes 2 commercial products (PACE and NBAR) and 4 open-source tools (OpenDPI, L7-filter, nDPI, and Libprotoident). We studied their performance in various scenarios (including packet and flow truncation) and at different classification levels (application protocol, application and web service). We carefully built a labeled dataset with more than 750 K flows, which contains traffic from popular applications. We used the Volunteer-Based System (VBS), developed at Aalborg University, to guarantee the correct labeling of the dataset. We released this dataset, including full packet payloads, to the research community. We believe this dataset could become a common benchmark for the comparison and validation of network traffic classifiers. Our results present PACE, a commercial tool, as the most accurate solution. Surprisingly, we find that some open-source tools, such as nDPI and Libprotoident, also achieve very high accuracy.Peer ReviewedPostprint (author’s final draft

Timely Classification of Encrypted or ProtocolObfuscated Internet Traffic Using Statistical Methods

Internet traffic classification aims to identify the type of application or protocol that generated a particular packet or stream of packets on the network. Through traffic classification, Internet Service Providers (ISPs), governments, and network administrators can access basic functions and several solutions, including network management, advanced network monitoring, network auditing, and anomaly detection. Traffic classification is essential as it ensures the Quality of Service (QoS) of the network, as well as allowing efficient resource planning. With the increase of encrypted or obfuscated protocol traffic on the Internet and multilayer data encapsulation, some classical classification methods have lost interest from the scientific community. The limitations of traditional classification methods based on port numbers and payload inspection to classify encrypted or obfuscated Internet traffic have led to significant research efforts focused on Machine Learning (ML) based classification approaches using statistical features from the transport layer. In an attempt to increase classification performance, Machine Learning strategies have gained interest from the scientific community and have shown promise in the future of traffic classification, specially to recognize encrypted traffic. However, ML approach also has its own limitations, as some of these methods have a high computational resource consumption, which limits their application when classifying large traffic or realtime flows. Limitations of ML application have led to the investigation of alternative approaches, including featurebased procedures and statistical methods. In this sense, statistical analysis methods, such as distances and divergences, have been used to classify traffic in large flows and in realtime. The main objective of statistical distance is to differentiate flows and find a pattern in traffic characteristics through statistical properties, which enable classification. Divergences are functional expressions often related to information theory, which measure the degree of discrepancy between any two distributions. This thesis focuses on proposing a new methodological approach to classify encrypted or obfuscated Internet traffic based on statistical methods that enable the evaluation of network traffic classification performance, including the use of computational resources in terms of CPU and memory. A set of traffic classifiers based on KullbackLeibler and JensenShannon divergences, and Euclidean, Hellinger, Bhattacharyya, and Wootters distances were proposed. The following are the four main contributions to the advancement of scientific knowledge reported in this thesis. First, an extensive literature review on the classification of encrypted and obfuscated Internet traffic was conducted. The results suggest that portbased and payloadbased methods are becoming obsolete due to the increasing use of traffic encryption and multilayer data encapsulation. MLbased methods are also becoming limited due to their computational complexity. As an alternative, Support Vector Machine (SVM), which is also an ML method, and the KolmogorovSmirnov and Chisquared tests can be used as reference for statistical classification. In parallel, the possibility of using statistical methods for Internet traffic classification has emerged in the literature, with the potential of good results in classification without the need of large computational resources. The potential statistical methods are Euclidean Distance, Hellinger Distance, Bhattacharyya Distance, Wootters Distance, as well as KullbackLeibler (KL) and JensenShannon divergences. Second, we present a proposal and implementation of a classifier based on SVM for P2P multimedia traffic, comparing the results with KolmogorovSmirnov (KS) and Chisquare tests. The results suggest that SVM classification with Linear kernel leads to a better classification performance than KS and Chisquare tests, depending on the value assigned to the Self C parameter. The SVM method with Linear kernel and suitable values for the Self C parameter may be a good choice to identify encrypted P2P multimedia traffic on the Internet. Third, we present a proposal and implementation of two classifiers based on KL Divergence and Euclidean Distance, which are compared to SVM with Linear kernel, configured with the standard Self C parameter, showing a reduced ability to classify flows based solely on packet sizes compared to KL and Euclidean Distance methods. KL and Euclidean methods were able to classify all tested applications, particularly streaming and P2P, where for almost all cases they efficiently identified them with high accuracy, with reduced consumption of computational resources. Based on the obtained results, it can be concluded that KL and Euclidean Distance methods are an alternative to SVM, as these statistical approaches can operate in realtime and do not require retraining every time a new type of traffic emerges. Fourth, we present a proposal and implementation of a set of classifiers for encrypted Internet traffic, based on JensenShannon Divergence and Hellinger, Bhattacharyya, and Wootters Distances, with their respective results compared to those obtained with methods based on Euclidean Distance, KL, KS, and ChiSquare. Additionally, we present a comparative qualitative analysis of the tested methods based on Kappa values and Receiver Operating Characteristic (ROC) curves. The results suggest average accuracy values above 90% for all statistical methods, classified as ”almost perfect reliability” in terms of Kappa values, with the exception of KS. This result indicates that these methods are viable options to classify encrypted Internet traffic, especially Hellinger Distance, which showed the best Kappa values compared to other classifiers. We conclude that the considered statistical methods can be accurate and costeffective in terms of computational resource consumption to classify network traffic. Our approach was based on the classification of Internet network traffic, focusing on statistical distances and divergences. We have shown that it is possible to classify and obtain good results with statistical methods, balancing classification performance and the use of computational resources in terms of CPU and memory. The validation of the proposal supports the argument of this thesis, which proposes the implementation of statistical methods as a viable alternative to Internet traffic classification compared to methods based on port numbers, payload inspection, and ML.A classificação de tráfego Internet visa identificar o tipo de aplicação ou protocolo que gerou um determinado pacote ou fluxo de pacotes na rede. Através da classificação de tráfego, Fornecedores de Serviços de Internet (ISP), governos e administradores de rede podem ter acesso às funções básicas e várias soluções, incluindo gestão da rede, monitoramento avançado de rede, auditoria de rede e deteção de anomalias. Classificar o tráfego é essencial, pois assegura a Qualidade de Serviço (QoS) da rede, além de permitir planear com eficiência o uso de recursos. Com o aumento de tráfego cifrado ou protocolo ofuscado na Internet e do encapsulamento de dados multicamadas, alguns métodos clássicos da classificação perderam interesse de investigação da comunidade científica. As limitações dos métodos tradicionais da classificação com base no número da porta e na inspeção de carga útil payload para classificar o tráfego de Internet cifrado ou ofuscado levaram a esforços significativos de investigação com foco em abordagens da classificação baseadas em técnicas de Aprendizagem Automática (ML) usando recursos estatísticos da camada de transporte. Na tentativa de aumentar o desempenho da classificação, as estratégias de Aprendizagem Automática ganharam o interesse da comunidade científica e se mostraram promissoras no futuro da classificação de tráfego, principalmente no reconhecimento de tráfego cifrado. No entanto, a abordagem em ML também têm as suas próprias limitações, pois alguns desses métodos possuem um elevado consumo de recursos computacionais, o que limita a sua aplicação para classificação de grandes fluxos de tráfego ou em tempo real. As limitações no âmbito da aplicação de ML levaram à investigação de abordagens alternativas, incluindo procedimentos baseados em características e métodos estatísticos. Neste sentido, os métodos de análise estatística, tais como distâncias e divergências, têm sido utilizados para classificar tráfego em grandes fluxos e em tempo real. A distância estatística possui como objetivo principal diferenciar os fluxos e permite encontrar um padrão nas características de tráfego através de propriedades estatísticas, que possibilitam a classificação. As divergências são expressões funcionais frequentemente relacionadas com a teoria da informação, que mede o grau de discrepância entre duas distribuições quaisquer. Esta tese focase na proposta de uma nova abordagem metodológica para classificação de tráfego cifrado ou ofuscado da Internet com base em métodos estatísticos que possibilite avaliar o desempenho da classificação de tráfego de rede, incluindo a utilização de recursos computacionais, em termos de CPU e memória. Foi proposto um conjunto de classificadores de tráfego baseados nas Divergências de KullbackLeibler e JensenShannon e Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters. A seguir resumemse os tese. Primeiro, realizámos uma ampla revisão de literatura sobre classificação de tráfego cifrado e ofuscado de Internet. Os resultados sugerem que os métodos baseados em porta e baseados em carga útil estão se tornando obsoletos em função do crescimento da utilização de cifragem de tráfego e encapsulamento de dados multicamada. O tipo de métodos baseados em ML também está se tornando limitado em função da complexidade computacional. Como alternativa, podese utilizar a Máquina de Vetor de Suporte (SVM), que também é um método de ML, e os testes de KolmogorovSmirnov e Quiquadrado como referência de comparação da classificação estatística. Em paralelo, surgiu na literatura a possibilidade de utilização de métodos estatísticos para classificação de tráfego de Internet, com potencial de bons resultados na classificação sem aporte de grandes recursos computacionais. Os métodos estatísticos potenciais são as Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters, além das Divergências de Kullback–Leibler (KL) e JensenShannon. Segundo, apresentamos uma proposta e implementação de um classificador baseado na Máquina de Vetor de Suporte (SVM) para o tráfego multimédia P2P (PeertoPeer), comparando os resultados com os testes de KolmogorovSmirnov (KS) e Quiquadrado. Os resultados sugerem que a classificação da SVM com kernel Linear conduz a um melhor desempenho da classificação do que os testes KS e Quiquadrado, dependente do valor atribuído ao parâmetro Self C. O método SVM com kernel Linear e com valores adequados para o parâmetro Self C pode ser uma boa escolha para identificar o tráfego Par a Par (P2P) multimédia cifrado na Internet. Terceiro, apresentamos uma proposta e implementação de dois classificadores baseados na Divergência de KullbackLeibler (KL) e na Distância Euclidiana, sendo comparados com a SVM com kernel Linear, configurado para o parâmestro Self C padrão, apresenta reduzida capacidade de classificar fluxos com base apenas nos tamanhos dos pacotes em relação aos métodos KL e Distância Euclidiana. Os métodos KL e Euclidiano foram capazes de classificar todas as aplicações testadas, destacandose streaming e P2P, onde para quase todos os casos foi eficiente identificálas com alta precisão, com reduzido consumo de recursos computacionais.Com base nos resultados obtidos, podese concluir que os métodos KL e Distância Euclidiana são uma alternativa à SVM, porque essas abordagens estatísticas podem operar em tempo real e não precisam de retreinamento cada vez que surge um novo tipo de tráfego. Quarto, apresentamos uma proposta e implementação de um conjunto de classificadores para o tráfego de Internet cifrado, baseados na Divergência de JensenShannon e nas Distâncias de Hellinger, Bhattacharyya e Wootters, sendo os respetivos resultados comparados com os resultados obtidos com os métodos baseados na Distância Euclidiana, KL, KS e Quiquadrado. Além disso, apresentamos uma análise qualitativa comparativa dos métodos testados com base nos valores de Kappa e Curvas Característica de Operação do Receptor (ROC). Os resultados sugerem valores médios de precisão acima de 90% para todos os métodos estatísticos, classificados como “confiabilidade quase perfeita” em valores de Kappa, com exceçãode KS. Esse resultado indica que esses métodos são opções viáveis para a classificação de tráfego cifrado da Internet, em especial a Distância de Hellinger, que apresentou os melhores resultados do valor de Kappa em comparaçãocom os demais classificadores. Concluise que os métodos estatísticos considerados podem ser precisos e económicos em termos de consumo de recursos computacionais para classificar o tráfego da rede. A nossa abordagem baseouse na classificação de tráfego de rede Internet, focando em distâncias e divergências estatísticas. Nós mostramos que é possível classificar e obter bons resultados com métodos estatísticos, equilibrando desempenho de classificação e uso de recursos computacionais em termos de CPU e memória. A validação da proposta sustenta o argumento desta tese, que propõe a implementação de métodos estatísticos como alternativa viável à classificação de tráfego da Internet em relação aos métodos com base no número da porta, na inspeção de carga útil e de ML.Thesis prepared at Instituto de Telecomunicações Delegação da Covilhã and at the Department of Computer Science of the University of Beira Interior, and submitted to the University of Beira Interior for discussion in public session to obtain the Ph.D. Degree in Computer Science and Engineering. This work has been funded by Portuguese FCT/MCTES through national funds and, when applicable, cofunded by EU funds under the project UIDB/50008/2020, and by operation Centro010145FEDER000019 C4 Centro de Competências em Cloud Computing, cofunded by the European Regional Development Fund (ERDF/FEDER) through the Programa Operacional Regional do Centro (Centro 2020). This work has also been funded by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education) within the Ministry of Education of Brazil under a scholarship supported by the International Cooperation Program CAPES/COFECUB Project 9090134/ 2013 at the University of Beira Interior

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%

Reviewing Traffic ClassificationData Traffic Monitoring and Analysis

Traffic classification has received increasing attention in the last years. It aims at offering the ability to automatically recognize the application that has generated a given stream of packets from the direct and passive observation of the individual packets, or stream of packets, flowing in the network. This ability is instrumental to a number of activities that are of extreme interest to carriers, Internet service providers and network administrators in general. Indeed, traffic classification is the basic block that is required to enable any traffic management operations, from differentiating traffic pricing and treatment (e.g., policing, shaping, etc.), to security operations (e.g., firewalling, filtering, anomaly detection, etc.). Up to few years ago, almost any Internet application was using well-known transport layer protocol ports that easily allowed its identification. More recently, the number of applications using random or non-standard ports has dramatically increased (e.g. Skype, BitTorrent, VPNs, etc.). Moreover, often network applications are configured to use well-known protocol ports assigned to other applications (e.g. TCP port 80 originally reserved for Web traffic) attempting to disguise their presence. For these reasons, and for the importance of correctly classifying traffic flows, novel approaches based respectively on packet inspection, statistical and machine learning techniques, and behavioral methods have been investigated and are becoming standard practice. In this chapter, we discuss the main trend in the field of traffic classification and we describe some of the main proposals of the research community. We complete this chapter by developing two examples of behavioral classifiers: both use supervised machine learning algorithms for classifications, but each is based on different features to describe the traffic. After presenting them, we compare their performance using a large dataset, showing the benefits and drawback of each approac

Controlling P2P File-Sharing Networks Traffic

Since the appearance of Peer-To-Peer (P2P) file-sharing networks some time ago, many Internet users have chosen this technology to share and search programs, videos, music, documents, etc. The total number of P2P file-sharing users has been increasing and decreasing in the last decade depending on the creation or end of some well known P2P file-sharing systems. P2P file-sharing networks traffic is currently overloading some data networks and it is a major headache for network administrators because it is difficult to control this kind of traffic (mainly because some P2P file-sharing networks encrypt their messages). This paper deals with the analysis, taxonomy and characterization of eight Public P2P file-sharing networks: Gnutella, Freeenet, Soulseek, BitTorrent, Opennap, eDonkey, MP2P and FastTrack. These eight most popular networks have been selected due to their different type of working architecture. Then, we will show the amount of users, files and the size of files inside these file-sharing networks. Finally, several network configurations are presented in order to control P2P file-sharing traffic in the network.García Pineda, M.; Hammoumi, M.; Canovas Solbes, A.; Lloret, J. (2011). Controlling P2P File-Sharing Networks Traffic. Network Protocols and Algorithms. 3(4):54-92. doi:10.5296/npa.v3i4.1365S54923