Offline/realtime traffic classification using semi-supervised learning

Erman, Jeffrey; Mahanti, Anirban; Arlitt, Martin; Cohen, Ira L.; Williamson, Carey

doi:10.1016/j.peva.2007.06.014

Cited by 252 publications

(173 citation statements)

References 26 publications

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“…Recent work by McGregor et al [13] and Zander et al [14] show that cluster analysis has the ability to group Internet traffic using only transport layer characteristics. Mahanti and co-workers [26], Quin et al [27], used semi-supervised learning methods to classify the network traffic and application.…”

Section: Related Research Literaturementioning

confidence: 99%

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

Chen

Yang

Chen

et al. 2009

Applied Soft Computing

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Section: Related Research Literaturementioning

confidence: 99%

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

Chen

Yang

Chen

et al. 2009

Applied Soft Computing

View full text Add to dashboard Cite

“…Also, this approach is more computationally expensive as the classifier needs to be trained and tested for each subset of features. An example of a wrapper method is the backward greedy feature selection used in [46]. This method starts by training and testing the classifier with n features.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Erman et al have proposed different types of clustering methods [46,48]. In [48], Erman et al experimented with the K-Means, Density Based Spatial Clustering of Applications with Noise (DBSCAN) and AutoClass [49] (which uses Expectation Maximization (EM)) traffic clustering algorithms.…”

Section: Clusteringmentioning

confidence: 99%

“…In [46], Erman et al expanded their work on K-Means clustering. The first thing that they expanded on is changing the problem from a supervised problem to a semisupervised one.…”

Section: Clusteringmentioning

confidence: 99%

“…The signatures we used to establish our ground truth can be found in Appendix A. Most of the signatures we used were based from the ones found in [88]. From these signatures we found four dominant applications inside our data trace.…”

Section: Data and Processingmentioning

confidence: 99%

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Controlling False Alarm/Discovery Rates in Online Internet Traffic Flow Classification

2009

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Classifying Internet traffic flows online into applications or broader classes without inspecting the packet payloads or without relying on port numbers has become a necessity for network operators. The operators can use this information to monitor their networks and provide per-class quality of service. There has been a great deal of research done on Internet traffic classification recently and numerous techniques have been proposed. While the current techniques can obtain a high accuracy classifying Internet traffic, providing performance guarantees for particular classes of interest has never been addressed. In this thesis, we provide two novel types of online Internet traffic classifiers that can provide performance guarantees on the false alarm and false discovery rates, respectively. These guarantees can be for an entire class (class-wise) or between two classes (pair-wise). Controlling false alarm rates is well-suited for application prioritization (i.e. prioritizing time-sensitive applications like VoIP over HTTP) whereas controlling false discovery rates is better suited for blocking or rate-limiting a targeted class of traffic (i.e. Peer-to-Peer). The classifier that provides false alarm rate guarantees is based on a Neyman-Pearson classification framework while the classifier that provides false discovery rate guarantees is based on the Learning to Satisfy (LSAT) framework. Both of these classifiers are implemented using a machine learning technique, namely, the 2-nu Support Vector Machine (SVM). Moreover, all previous work done with these two statistical methodologies focused on binary classification only; we extend these statistical methodologies to a multi-class setting. In addition to the regular application classification problem, we also present preliminary work on a binary LSAT classifier that can detect, after the reception of only a handful of packets, whether a flow will be large, as defined by a network operator. This large flow detector can act as a preprocessor for regular application classifiers. By allowing only large flows to pass to the classifier, this allows the classifier to focus on the more resource-intensive flows. We validated our Internet traffic classifiers by testing our approaches using data provided by an ISP.ii Abrégé Identifier l'application (ou autre classe plus générale) qui génère un flux de trafic Internet, sans compter sur le numéro du port ou inspecter la charge des paquets, est devenu une nécessité pour les opérateurs de réseau. Les opérateurs peuvent utiliser cette information pour surveiller leurs réseaux et fournir une qualité de service propreà chaque classe. Il y a eu beaucoup de travaux de recherche portant sur la classification du trafic Internet effectué récemment et de nombreuses techniques ontété proposées. Bien que les techniques actuelles puissent obtenir une grande précision pour classer le trafic Internet, offrir des garanties de performance pour des catégories particulières est un problème encore inexploré.Dans ce mémoire, nous proposons deu...

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UARA in edge routers: an effective approach to user fairness and traffic shaping

Khosroshahy

2011

Int J Communication

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SUMMARY The ever‐increasing share of the peer‐to‐peer (P2P) traffic flowing in the Internet has unleashed new challenges to the quality of service provisioning. Striving to accommodate the rise of P2P traffic or to curb its growth has led to many schemes being proposed: P2P caches, P2P filters, ALTO mechanisms and re‐ECN. In this paper, we propose a scheme named ‘UARA:textbfUser/Application‐aware RED‐based AQM’ which has a better perspective on the problem: UARA is proposed to be implemented at the edge routers providing real‐time near‐end‐user traffic shaping and congestion avoidance. UARA closes the loopholes exploited by the P2P traffic by bringing under control the P2P users who open and use numerous simultaneous connections. In congestion times, UARA monitors the flows of each user and caps the bandwidth used by ‘power users’ which leads to the fair usage of network resources. While doing so, UARA also prioritizes the real‐time traffic of each user, further enhancing the average user quality of experience (QoE). UARA hence centralizes three important functionalities at the edge routers: (1) congestion avoidance; (2) providing user fairness; (3) prioritizing real‐time traffic. The simulation results indicate that average user QoE is significantly improved in congestion times with UARA at the edge routers. Copyright © 2011 John Wiley & Sons, Ltd.

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Offline/realtime traffic classification using semi-supervised learning

Cited by 252 publications

References 26 publications

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

Controlling False Alarm/Discovery Rates in Online Internet Traffic Flow Classification

UARA in edge routers: an effective approach to user fairness and traffic shaping

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