Detection of Burst Header Packet Flooding Attack in Optical Burst Switching Networks Using Gorilla Troops Optimizer and Extreme Learning Machine

Abstract

The software part plays a very important role in Moderns network architectures. As a result, they become very vulnerable to attacks. In Algeria, burst optical switching networks are widely used. Cyber attackers frequently conduct Burst Header Packet flooding attacks. Detecting this type of attack will help prevent the limitation or termination of services. To solve this problem, we propose in this work a new approach that combines Gorilla Troops Optimizer and Extreme Learning Machine. To implement our approach, we used the publish/subscribe paradigm. We carried out a comparative study between several machine learning methods to prove the quality of the proposed method.

Keywords: Gorilla Troops Optimizer, Extreme Learning Machine, Predictive models, Optical fiber networks, Optical burst switching