Applying Support Vector Machine to P2P Traffic Identification with Smooth Processing

Liu, Yang; Wang, Rui; Huang, Heyun; Ying-sheng, Zeng; He, Hangen

doi:10.1109/icosp.2006.345921

Cited by 4 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Lagrangian has to be minimized with respect to , w b and maximized with respect to 0 α ≥ . Classical Lagrangian duality enables the primal problem, given by equation (6), to be transformed to its dual problem, which is easier to solve. The dual problem is given by:…”

Section: Fig 2 Optimal Separating Hyperplane and Support Vectorsmentioning

confidence: 99%

“…Recent studies describe many ongoing attempts to apply data mining and machine learning techniques to Internet application traffic classification in order to cope with application changes [4][5] [6] [7][8] [9]. Since machine learning techniques (supervised or unsupervised learning) can extract important patterns from the feature vectors, which are independent of dramatically changing port numbers and encrypted payloads, those techniques have emerged as viable methods to cope with the aforementioned problems of traditional methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the application of a data mining method is appropriate considering that the huge volume of stream data is a characteristic of network traffic. The Support Vector Machine (SVM), which has emerged as a promising tool in the area of intelligent systems, has received attention by researchers on its application to Internet traffic classification [6] [7][8] [9]. However, employing SVMs in the Internet application traffic classification problem is at a relatively early stage.…”

Section: Introductionmentioning

confidence: 99%

“…It involves two methodologies. In the first method [6] [7], P2P and non-P2P traffic is simply divided using an SVM (a binary classifier) for the detection of P2P traffic. In the second method [8][9] [10], P2P traffic identification and traffic classification involve organically combining various binary classifier SVMs, as with the one-against-one method, then building a multi-class SVM.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM

Yu¹

2010

KSII TIIS

View full text Add to dashboard Cite

In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse-and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.

show abstract

Section: Fig 2 Optimal Separating Hyperplane and Support Vectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM

Yu¹

2010

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…최근의 연구문헌 조사에 의하면, 애플리케이션의 변화에 대처할 수 있는 새로운 해결책으로써 데이터마이닝 및 기계 학습 기법을 인터넷 애플리케이션 트래픽 분류에 적용하려 는 시도가 성공적으로 진행 중이다 [3][4][5][6][7][8] …”

Section: 서 론unclassified

Hierarchical Internet Application Traffic Classification using a Multi-class SVM

유재학¹,

이한성²,

임영희³

et al. 2010

Journal of Korean Institute of Intelligent Systems

View full text Add to dashboard Cite

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.

show abstract

Research of Initiative Monitoring Model Based on Hybrid P2P Network for Special Information

Ding

Cai

2010

2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing

View full text Add to dashboard Cite

Applying Support Vector Machine to P2P Traffic Identification with Smooth Processing

Cited by 4 publications

References 9 publications

Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM

Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM

Hierarchical Internet Application Traffic Classification using a Multi-class SVM

Research of Initiative Monitoring Model Based on Hybrid P2P Network for Special Information

Contact Info

Product

Resources

About