Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification

B. M.A. Abdalla; Mosab Hamdan; Entisar H. Khalifa; Abdallah Elhigazi; Ismahani Ismail; M. N. Marsono

doi:10.1109/SCOReD50371.2020.9250967

Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification

B. M.A. Abdalla
, Mosab Hamdan
, Entisar H. Khalifa
, Abdallah Elhigazi
, Ismahani Ismail
, M. N. Marsono

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Managing high-bandwidth application traffic through identification of bandwidth-heavy Internet traffic is important for network administration. classification based on statistical flow features was proven as an encouraging method for identifying Internet traffic. Early estimation of statistical flow features from first n packets still plays an essential role in accurate and timely traffic classification. In this work, we investigate the impact of early estimation of statistical flow features for on-line P2P classification in terms of accuracy, Kappa statistic and classification time. Simulations were conducted using available traces from the University of Brescia. Results illustrate the early statistical flow features estimation for gives the most significant accuracy and efficiency to detect P2P traffic.

Original language	English
Title of host publication	2020 IEEE Student Conference on Research and Development, SCOReD 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	294-299
Number of pages	6
ISBN (Electronic)	9781728193175
DOIs	https://doi.org/10.1109/SCOReD50371.2020.9250967
State	Published - 27 Sep 2020
Externally published	Yes
Event	2020 IEEE Student Conference on Research and Development, SCOReD 2020 - Virtual, Johor, Malaysia Duration: 27 Sep 2020 → 28 Sep 2020

Publication series

Name	2020 IEEE Student Conference on Research and Development, SCOReD 2020

Conference

Conference	2020 IEEE Student Conference on Research and Development, SCOReD 2020
Country/Territory	Malaysia
City	Virtual, Johor
Period	27/09/20 → 28/09/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Classification
Machine learning
Statistical flow features

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Human-Computer Interaction
Electrical and Electronic Engineering
Media Technology
Waste Management and Disposal
Control and Optimization

Access to Document

10.1109/SCOReD50371.2020.9250967

Cite this

Abdalla, B. M. A., Hamdan, M., Khalifa, E. H., Elhigazi, A., Ismail, I., & Marsono, M. N. (2020). Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification. In 2020 IEEE Student Conference on Research and Development, SCOReD 2020 (pp. 294-299). Article 9250967 (2020 IEEE Student Conference on Research and Development, SCOReD 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCOReD50371.2020.9250967

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title = "Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification",

abstract = "Managing high-bandwidth application traffic through identification of bandwidth-heavy Internet traffic is important for network administration. classification based on statistical flow features was proven as an encouraging method for identifying Internet traffic. Early estimation of statistical flow features from first n packets still plays an essential role in accurate and timely traffic classification. In this work, we investigate the impact of early estimation of statistical flow features for on-line P2P classification in terms of accuracy, Kappa statistic and classification time. Simulations were conducted using available traces from the University of Brescia. Results illustrate the early statistical flow features estimation for gives the most significant accuracy and efficiency to detect P2P traffic.",

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language = "English",

series = "2020 IEEE Student Conference on Research and Development, SCOReD 2020",

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Abdalla, BMA, Hamdan, M, Khalifa, EH, Elhigazi, A, Ismail, I & Marsono, MN 2020, Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification. in 2020 IEEE Student Conference on Research and Development, SCOReD 2020., 9250967, 2020 IEEE Student Conference on Research and Development, SCOReD 2020, Institute of Electrical and Electronics Engineers Inc., pp. 294-299, 2020 IEEE Student Conference on Research and Development, SCOReD 2020, Virtual, Johor, Malaysia, 27/09/20. https://doi.org/10.1109/SCOReD50371.2020.9250967

Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification. / Abdalla, B. M.A.; Hamdan, Mosab; Khalifa, Entisar H. et al.
2020 IEEE Student Conference on Research and Development, SCOReD 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 294-299 9250967 (2020 IEEE Student Conference on Research and Development, SCOReD 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Abdalla, B. M.A.

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AU - Khalifa, Entisar H.

AU - Elhigazi, Abdallah

AU - Ismail, Ismahani

AU - Marsono, M. N.

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AB - Managing high-bandwidth application traffic through identification of bandwidth-heavy Internet traffic is important for network administration. classification based on statistical flow features was proven as an encouraging method for identifying Internet traffic. Early estimation of statistical flow features from first n packets still plays an essential role in accurate and timely traffic classification. In this work, we investigate the impact of early estimation of statistical flow features for on-line P2P classification in terms of accuracy, Kappa statistic and classification time. Simulations were conducted using available traces from the University of Brescia. Results illustrate the early statistical flow features estimation for gives the most significant accuracy and efficiency to detect P2P traffic.

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KW - Machine learning

KW - Statistical flow features

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ER -

Abdalla BMA, Hamdan M, Khalifa EH, Elhigazi A, Ismail I, Marsono MN. Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification. In 2020 IEEE Student Conference on Research and Development, SCOReD 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 294-299. 9250967. (2020 IEEE Student Conference on Research and Development, SCOReD 2020). doi: 10.1109/SCOReD50371.2020.9250967

Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this