Detection of Flooding Attack on OBS Network Using ant Colony Optimization and Machine Learning

Authors

  • Mohamed Takieddine Seddik University Batna
  • Ouahab Kadri University Batna
  • Chakir Bouarouguene University Batna
  • Houssem Brahimi University Batna

DOI:

https://doi.org/10.13053/cys-25-2-3939

Keywords:

Optical burst switching, support vector machine, extreme learning machine, burst header packet, cloud computing

Abstract

Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS). However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.

Author Biographies

Mohamed Takieddine Seddik, University Batna

Faculty of MI, Department of Computer Science

Ouahab Kadri, University Batna

Laboratory of Automation and Manufacturing Engineering

Chakir Bouarouguene, University Batna

Faculty of MI, Department of Computer Science

Houssem Brahimi, University Batna

Faculty of MI, Department of Computer Science

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Published

2021-05-01

Issue

Vol. 25 No. 2 (2021): 25(2) 2021

Section

Articles

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