A P2P Traffic Classification Method Based on SVM

Xusheng, Zhou

doi:10.1109/iscsct.2008.368

Cited by 17 publications

(8 citation statements)

References 3 publications

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“…Some such methods were proposed to classify various Internet applications: in [5] an approach to recognize P2P traffic (amongst BitTorrent, PPLive, Skype and MSN Messenger) based on the Support Vector Machine (SVM) is suggested. A Naïve Bayes estimator to categorize traffic by application is instead proposed in [6], and a packet-level traffic classification approach based on Hidden Markov Model (HMM) is presented in [7].…”

Section: Related Workmentioning

confidence: 99%

A decision theory based tool for detection of encrypted WebRTC traffic

Mauro

Longo

2015

2015 18th International Conference on Intelligence in Next Generation Networks

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The detection of encrypted streamed traffic (like VoIP or Video) is an increasingly important issue for authorities involved in lawful interception. Aside from well established technologies like Skype, Facetime and MSN Messenger a new one is recently spreading: Web Real-Time Communication (WebRTC), which, with the support of powerful encryption methods as DTLS, offers capabilities for encrypted streaming voice and video without the need of installing a specific application but using a common browser like Chrome, Firefox or Opera. WebRTC traffic cannot be detected through methods of semantic recognition since it does not exhibit a distinguishable sequence of information pieces and hence statistical recognition methods are called for. In this paper we propose and evaluate a decision theory based system allowing to recognize encrypted WebRTC traffic by means of an open-source machine learning environment: Weka.

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Section: Related Workmentioning

confidence: 99%

A decision theory based tool for detection of encrypted WebRTC traffic

Mauro

Longo

2015

2015 18th International Conference on Intelligence in Next Generation Networks

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“…In recent years, there has been a lot of researches and applications for classifying network traffic with the SVM algorithm [3,4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

P2P traffic identification method based on an improvement incremental SVM learning algorithm

Gong

Wang

et al. 2014

2014 International Symposium on Wireless Personal Multimedia Communications (WPMC)

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How to classify the data sets with vast information amount and large distribution fluctuation, which is always the research hotspot. This paper puts forward an improved SVM incremental learning algorithm by comparing the different incremental learning methods of SVM algorithm. In the algorithm, whether to violate the KTT conditions is regarded as an important basis for incremental data set. And the algorithm will be more efficient on the classification of SVM incremental sets through optimizing and improving itself. Then the paper compares the SVM-based re-training algorithm, the standard SVM incremental learning algorithm and the improved SVM incremental learning algorithm through identifying P2P network traffic. The experimental results show that the improved SVM incremental learning algorithm proposed in this article can save storage space and increase the accuracy of the identification of P2P traffic.

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“…Network Traffic Classification refers to the classification of TCP flow or UDP, in term of the application type of network based on the internet TCP/IP protocol [1].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, some scholars do researches on traffic classification by using Machine Learning [1]. For the dynamic statistical feature of P2P traffic, machine learning technology is a potential useful solution for effective identification [13].…”

Section: Introductionmentioning

confidence: 99%

A New Method of P2P Traffic Identification Based on Support Vector Machine at the Host Level

Liu

Nie

2009

2009 International Conference on Information Technology and Computer Science

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These years, P2P applications have multiplied, evolved and take a big part of Internet traffic workload. Identifying the P2P traffic and understanding their behavior is an important field. Some port, payload and transport layer feature based methods were proposed. P2P traffic identification methods by examining user payload or well-defined port numbers no longer adapt to current P2P applications. In recent years, some scholars do researches on traffic classification by using Machine Learning. However, previous researches are almost on the flow level. In this paper, we develop a new method of P2P traffic identification based on Support Vector Machine by analyzing packet length, remote hosts' discreteness, connection responded success rate and the ratio of IP and port at the host level without relying on the port numbers and packet payload. Finally, the experiment results indicate that this approach can effectively identify P2P applications.

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A P2P Traffic Classification Method Based on SVM

Cited by 17 publications

References 3 publications

A decision theory based tool for detection of encrypted WebRTC traffic

A decision theory based tool for detection of encrypted WebRTC traffic

P2P traffic identification method based on an improvement incremental SVM learning algorithm

A New Method of P2P Traffic Identification Based on Support Vector Machine at the Host Level

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