A Novel Approach of DDOS Attack Classification with Optimizing the Ensemble Classifier Using A Hybrid Firefly and Particle Swarm Optimization (HFPSO)

Abstract

Cyberspace is fraught with dangers, one of which is the distributed denial of service attack (DDoS). This type of attack is particularly concerning as it can disrupt vital services, prevent authorized users from accessing them, and result in financial losses. The aim of this research is to present an optimized AdaBoost classifier that has been fine-tuned using the HFPSO algorithm. The data is pre-processed to ensure that it conforms to standard features by normalizing it. Additionally, cross-correlation techniques are used to select features in order to eliminate redundancy. Finally, the constructed signals are used to train and test an HFPSO-optimized AdaBoost classifier. The result indicates the possibility of anticipating the attacks is fairly accurate. The system accuracy is 99.97%.