Internet Traffic Classification Using Score Level Fusion of Multiple Classifier

Ichino, Masatsugu; Maeda, Hiroaki; Yamashita, Takeshi; Hoshi, Kentaro; Komatsu, Nobuyoshi; Takeshita, Keizo; Tsujino, Masayuki; Iwashita, Motoi; Yoshino, Hideaki

doi:10.1109/icis.2010.48

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…When input traffic data is not classified into own application, own application is in the priority candidate. This means that using score level fusion [20] with several classifiers and the LPC cepstrum may improve the accuracy of application traffic classification if an appropriate decision boundary is used for the score map. Using more than three scores may also improve the accuracy.…”

Section: Discussionmentioning

confidence: 99%

Internet Traffic Classification Using LPC Cepstrum

Ichino

Maeda

Hoshi

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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Internet traffic volumes continue to increase rapidly with the spread of the broadband access and the increase in internet backbone capacities. Moreover, the applications provided over the internet have become more diverse. Adequately supporting the QoS requirements for these diverse applications requires measuring traffic volumes by application type(file transfer, broadcast and so on). Therefore, methods for classifying applications become extremely important for multi QoS environment. In some application traffic classification methods, the applications are analyzed on the basis of statistics for the traffic characteristics, such as the packet inter arrival time and packet size.In this paper, we propose application traffic classification method applying LPC cepstrum. The proposed method enables to treat the traffic data as a time series signal and to separate spectral envelope information and spectral fine structure information from spectrum. We show that it is effective to use the LPC cepstrum to classify internet traffic by comparing LPC cepstrum with flow and raw data in this paper.

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Section: Discussionmentioning

confidence: 99%

Internet Traffic Classification Using LPC Cepstrum

Ichino

Maeda

Hoshi

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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“…g) Genetic algorithms: Sequential Forward Selection (SFS) [31], Symbiotic bid-based genetic programming [32], Multi-Objective Genetic Algorithm [33]. h) Miscellaneous: Pearson's Chi-Squared test [34], Logistic regression model [35], Adaboost [20], score level fusion using multiple implementations of the LindeBuzoGray (LBG)+Splitting algorithm [36].…”

Section: Related Workmentioning

confidence: 99%

Radio Frequency Traffic Classification Over WLAN

Kornycky

Abdul-Hameed

Kondoz

et al. 2017

IEEE/ACM Trans. Networking

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“… was shown to be robust against bias towards any scenario, which was an issue with previous classification algorithms. Nevertheless, future multi‐classifiers should be developed using more complex decision systems that use confidence values and intelligent combination algorithms . This should involve a wider range of techniques, including DPI, Auto‐class and decision trees.…”

Section: Toward An Optimal Traffic Classification Modelmentioning

confidence: 99%

A multilevel taxonomy and requirements for an optimal traffic‐classification model

2014

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Identifying Internet traffic applications is essential for network security and management. The steady emergence of new Internet applications, together with the use of encryption and obfuscation techniques, ensures that traffic classification remains a hot research topic. Much research has been devoted to this topic by the research community in the last decade. However, an optimal traffic classification model has yet to be defined. Many techniques and formats have been described, with the current literature therefore lacking appropriate benchmarks expressed in a consistent terminology. Moreover, existing surveys are outdated and do not include many recent advances in the field. In this article, we present a systematic multilevel taxonomy that covers a broad range of existing and recently proposed methods, together with examples of vendor classification techniques. Our taxonomy assists in defining a consistent terminology. It could be useful in future benchmarking contexts by characterizing and comparing methods at three different levels. From this perspective, we describe key features and provide design hints for future classification models, while emphasizing the main requirements for promoting future research efforts. To motivate researchers and other interested parties, we collect and share data captured from real traffic, using two models to protect data privacy.

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Internet Traffic Classification Using Score Level Fusion of Multiple Classifier

Cited by 6 publications

References 11 publications

Internet Traffic Classification Using LPC Cepstrum

Internet Traffic Classification Using LPC Cepstrum

Radio Frequency Traffic Classification Over WLAN

A multilevel taxonomy and requirements for an optimal traffic‐classification model

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