Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification

Alhamza Munther; Imad J. Mohammed; Mohammed Anbar; Anwer Mustafa Hilal

doi:10.1007/978-981-15-2693-0_12

Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification

Alhamza Munther
, Imad J. Mohammed
, Mohammed Anbar
, Anwer Mustafa Hilal

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

7 Scopus citations

Abstract

Supervised machine learning is a method to predict a class for labeled data, to improve different QoS metrics of several scopes such as educational, industrial and medical etc. This paper presents in-deep study focusing on four supervised classifiers were used widely to distinguish or categorize TCP/IP network traffic model and how they can be employed, these four are Naïve Bayes, Probabilistic Neural Network, Support Vector Machine and C4.5 decision tree. The classifiers are compared with regard to three significant metrics namely classification accuracy, classification speed and memory consumption. The implementation results of simulation and comparisons show that C4.5 decision tree introduce best results with high accuracy up to 99.6% using the benchmark dataset consist of 24863 packets compared to the rest three tested classifiers.

Original language	English
Title of host publication	Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers
Editors	Mohammed Anbar, Nibras Abdullah, Selvakumar Manickam
Publisher	Springer
Pages	168-181
Number of pages	14
ISBN (Print)	9789811526923
DOIs	https://doi.org/10.1007/978-981-15-2693-0_12
State	Published - 2020
Externally published	Yes
Event	1st International Conference on Advances in Cybersecurity, ACeS 2019 - George Town, Malaysia Duration: 30 Jul 2019 → 1 Aug 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1132 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Advances in Cybersecurity, ACeS 2019
Country/Territory	Malaysia
City	George Town
Period	30/07/19 → 1/08/19

Keywords

Internet traffic classification
Internet traffic engineering
Network machine learning
Supervised learning

Access to Document

10.1007/978-981-15-2693-0_12

Cite this

Munther, A., Mohammed, I. J., Anbar, M., & Hilal, A. M. (2020). Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification. In M. Anbar, N. Abdullah, & S. Manickam (Eds.), Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers (pp. 168-181). (Communications in Computer and Information Science; Vol. 1132 CCIS). Springer. https://doi.org/10.1007/978-981-15-2693-0_12

Munther, Alhamza ; Mohammed, Imad J. ; Anbar, Mohammed et al. / Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification. Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers. editor / Mohammed Anbar ; Nibras Abdullah ; Selvakumar Manickam. Springer, 2020. pp. 168-181 (Communications in Computer and Information Science).

@inproceedings{f64661012dac4f599b9c6abeb857af3b,

title = "Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification",

abstract = "Supervised machine learning is a method to predict a class for labeled data, to improve different QoS metrics of several scopes such as educational, industrial and medical etc. This paper presents in-deep study focusing on four supervised classifiers were used widely to distinguish or categorize TCP/IP network traffic model and how they can be employed, these four are Na{\"i}ve Bayes, Probabilistic Neural Network, Support Vector Machine and C4.5 decision tree. The classifiers are compared with regard to three significant metrics namely classification accuracy, classification speed and memory consumption. The implementation results of simulation and comparisons show that C4.5 decision tree introduce best results with high accuracy up to 99.6\% using the benchmark dataset consist of 24863 packets compared to the rest three tested classifiers.",

keywords = "Internet traffic classification, Internet traffic engineering, Network machine learning, Supervised learning",

author = "Alhamza Munther and Mohammed, \{Imad J.\} and Mohammed Anbar and Hilal, \{Anwer Mustafa\}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 1st International Conference on Advances in Cybersecurity, ACeS 2019 ; Conference date: 30-07-2019 Through 01-08-2019",

year = "2020",

doi = "10.1007/978-981-15-2693-0\_12",

language = "English",

isbn = "9789811526923",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "168--181",

editor = "Mohammed Anbar and Nibras Abdullah and Selvakumar Manickam",

booktitle = "Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers",

address = "Germany",

}

Munther, A, Mohammed, IJ, Anbar, M & Hilal, AM 2020, Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification. in M Anbar, N Abdullah & S Manickam (eds), Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers. Communications in Computer and Information Science, vol. 1132 CCIS, Springer, pp. 168-181, 1st International Conference on Advances in Cybersecurity, ACeS 2019, George Town, Malaysia, 30/07/19. https://doi.org/10.1007/978-981-15-2693-0_12

Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification. / Munther, Alhamza; Mohammed, Imad J.; Anbar, Mohammed et al.
Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers. ed. / Mohammed Anbar; Nibras Abdullah; Selvakumar Manickam. Springer, 2020. p. 168-181 (Communications in Computer and Information Science; Vol. 1132 CCIS).

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

TY - GEN

T1 - Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification

AU - Munther, Alhamza

AU - Mohammed, Imad J.

AU - Anbar, Mohammed

AU - Hilal, Anwer Mustafa

PY - 2020

Y1 - 2020

N2 - Supervised machine learning is a method to predict a class for labeled data, to improve different QoS metrics of several scopes such as educational, industrial and medical etc. This paper presents in-deep study focusing on four supervised classifiers were used widely to distinguish or categorize TCP/IP network traffic model and how they can be employed, these four are Naïve Bayes, Probabilistic Neural Network, Support Vector Machine and C4.5 decision tree. The classifiers are compared with regard to three significant metrics namely classification accuracy, classification speed and memory consumption. The implementation results of simulation and comparisons show that C4.5 decision tree introduce best results with high accuracy up to 99.6% using the benchmark dataset consist of 24863 packets compared to the rest three tested classifiers.

AB - Supervised machine learning is a method to predict a class for labeled data, to improve different QoS metrics of several scopes such as educational, industrial and medical etc. This paper presents in-deep study focusing on four supervised classifiers were used widely to distinguish or categorize TCP/IP network traffic model and how they can be employed, these four are Naïve Bayes, Probabilistic Neural Network, Support Vector Machine and C4.5 decision tree. The classifiers are compared with regard to three significant metrics namely classification accuracy, classification speed and memory consumption. The implementation results of simulation and comparisons show that C4.5 decision tree introduce best results with high accuracy up to 99.6% using the benchmark dataset consist of 24863 packets compared to the rest three tested classifiers.

KW - Internet traffic classification

KW - Internet traffic engineering

KW - Network machine learning

KW - Supervised learning

UR - https://www.scopus.com/pages/publications/85079238262

U2 - 10.1007/978-981-15-2693-0_12

DO - 10.1007/978-981-15-2693-0_12

M3 - Conference contribution

AN - SCOPUS:85079238262

SN - 9789811526923

T3 - Communications in Computer and Information Science

SP - 168

EP - 181

BT - Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers

A2 - Anbar, Mohammed

A2 - Abdullah, Nibras

A2 - Manickam, Selvakumar

PB - Springer

T2 - 1st International Conference on Advances in Cybersecurity, ACeS 2019

Y2 - 30 July 2019 through 1 August 2019

ER -

Munther A, Mohammed IJ, Anbar M, Hilal AM. Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification. In Anbar M, Abdullah N, Manickam S, editors, Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers. Springer. 2020. p. 168-181. (Communications in Computer and Information Science). doi: 10.1007/978-981-15-2693-0_12

Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification

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