Artificial immune system inspired algorithm for flow-based internet traffic classification

Brian Schmidt, Dionysios Kountanis, Ala Al-Fuqaha

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Internet traffic classification has been researched extensively in the last 10 years, with a few different algorithms applied to it. Internet traffic classification has also become more relevant because of its potential applications in the business world. Having information about network traffic has many benefits in network design, security, management, and accounting. The classification of network traffic is most easily achieved by Machine Learning algorithms, which can automatically build a model from training data, without much input from humans. Artificial Immune System classification algorithms have been used previously to classify network connections in network security systems [1]. They have proven to be very versatile, as well as having low sensitivity to input parameters. Because of this we are encouraged to explore the value of AIS algorithms to the Internet traffic classification problem. In this research, we propose an AIS-inspired algorithm for flow-based traffic classification, where each network flow is classified into an application class. We measure the algorithm's performance with and without the use of kernel functions, using a publicly available data set. We also compare the algorithm's performance with SVM and Naive Bayes classifiers. The algorithm generalizes well and gives high accuracy even with a small training set when compared to other algorithms, although the training and classification times were higher. The algorithm is also insensitive to the use of kernels, which makes it attractive for embedded and IoT applications.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, CloudCom 2014
PublisherIEEE Computer Society
Pages664-667
Number of pages4
EditionFebruary
ISBN (Electronic)9781479940936
DOIs
Publication statusPublished - 9 Feb 2015
Externally publishedYes
Event2014 6th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2014 - Singapore, Singapore
Duration: 15 Dec 201418 Dec 2014

Publication series

NameProceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
NumberFebruary
Volume2015-February
ISSN (Print)2330-2194
ISSN (Electronic)2330-2186

Conference

Conference2014 6th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2014
Country/TerritorySingapore
CitySingapore
Period15/12/1418/12/14

Keywords

  • Artificial immune systems
  • Internet traffic classification
  • Machine learning
  • Multi-class classification

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