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

Mohamed Takieddine Seddik, Ouahab Kadri, Chakir Bouarouguene, Houssem Brahimi


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.


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

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