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Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification

  • Alhamza MuntherEmail author
  • Imad J. Mohammed
  • Mohammed Anbar
  • Anwer Mustafa HilalEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1132)

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.

Keywords

Network machine learning Supervised learning Internet traffic engineering Internet traffic classification 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.IT Department, Sur College of Applied ScienceMinistry of Higher EducationSurSultanate of Oman
  2. 2.Department of Computer Science – College of ScienceUniversity of BaghdadBaghdadIraq
  3. 3.National Advanced IPv6 Centre of ExcellenceUniversiti Sains MalaysiaPenangMalaysia
  4. 4.Department of Computer and Self DevelopmentPrince Sattam Bin Abdulaziz UniversityKharjKingdom of Saudi Arabia

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