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A Performance Study of Hidden Markov Model and Random Forest in Internet Traffic Classification

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Information Science and Applications (ICISA) 2016

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 376))

Abstract

Currently, the emphasis on internet traffic classification is gaining momentum and a broad range of procedures has been developed for a variety of network tools. Machine learning methods are commonly utilized for the recognition and classification of internet traffic. This investigation trains its sights on two frequently employed techniques in this area. These are the Hidden Markov Model and the Random Forest procedure. Furthermore, the study offers a detailed explanation and analysis for the methodologies for each and forwards the advantages and setbacks attributed through comparing the performance in different significant factors.

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Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia

    Alhamza Munther & Rozmie R. Othman

  2. Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, 40292, USA

    Ali Shanon Alsaadi

  3. National Advanced IPv6 Centre of Excellence, Universiti Sains Malaysia, Gelugor, Penang, Malaysia

    Mohammed Anbar

Authors
  1. Alhamza Munther
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  2. Rozmie R. Othman
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  3. Ali Shanon Alsaadi
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  4. Mohammed Anbar
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Corresponding author

Correspondence to Alhamza Munther .

Editor information

Editors and Affiliations

  1. Kyonggi University, iCatse Kyonggi University, Seongnam-si, Kyonggi-do, Korea (Republic of)

    Kuinam J. Kim

  2. Chair of IEEE CS STCOS, IBM T.J.Watson Research Center Chair of IEEE CS STCOS, Yorktown Heights, New York, USA

    Nikolai Joukov

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© 2016 Springer Science+Business Media Singapore

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Munther, A., Othman, R.R., Alsaadi, A.S., Anbar, M. (2016). A Performance Study of Hidden Markov Model and Random Forest in Internet Traffic Classification. In: Kim, K., Joukov, N. (eds) Information Science and Applications (ICISA) 2016. Lecture Notes in Electrical Engineering, vol 376. Springer, Singapore. https://doi.org/10.1007/978-981-10-0557-2_32

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  • DOI: https://doi.org/10.1007/978-981-10-0557-2_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0556-5

  • Online ISBN: 978-981-10-0557-2

  • eBook Packages: EngineeringEngineering (R0)

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