A novel self-learning architecture for p2p traffic classification in high speed networks

Keralapura, Ram; Nucci, Antonio; Chuah, Chen‐Nee

doi:10.1016/j.comnet.2009.10.009

Cited by 35 publications

(30 citation statements)

References 13 publications

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“…To address all these, system architecture is configured suitably with self learning [4] Support/health/Critical parameter details, etc,. on to local Database or to a external server Database using built in Network interface.…”

Section: Sequence Of Operationmentioning

confidence: 99%

Self Learning Architecture Based Product Support Systems of Fighter Aircraft for Improved Performance

Vanamala¹,

Saravanan²,

Patel³

et al. 2015

May 13-14, 2015 Kuala Lumpur, Malaysia

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Section: Sequence Of Operationmentioning

confidence: 99%

Self Learning Architecture Based Product Support Systems of Fighter Aircraft for Improved Performance

Vanamala¹,

Saravanan²,

Patel³

et al. 2015

May 13-14, 2015 Kuala Lumpur, Malaysia

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“…It will not be affected by the payload encryption and can scale high to speed links. But the approach cannot do an accurate classification for P2P traffic due to the approach is only applied to analysis of traffic records, so it is not real time and efficient [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm

Wang

et al. 2015

Frontiers in Internet Technologies

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Abstract. As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

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“…In the past few years, P2P applications consume a large amount of Internet network bandwidth [3]. P2P traffic identification can make Internet service providers (ISPs) better manage large network and improve the quality of network service [4].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Trust Sampling Method for P2P Traffic Inspection

Chen¹,

Yu²,

Wang³

et al. 2013

IJGDC

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A novel self-learning architecture for p2p traffic classification in high speed networks

Cited by 35 publications

References 13 publications

Self Learning Architecture Based Product Support Systems of Fighter Aircraft for Improved Performance

Self Learning Architecture Based Product Support Systems of Fighter Aircraft for Improved Performance

P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm

An Adaptive Trust Sampling Method for P2P Traffic Inspection

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