A Prevalent Model-based on Machine Learning for Identifying DRDoS Attacks through Features Optimization Technique

Abstract

Growing apprehension among internet users regarding cyber-security threats, particularly Distributed Reflective Denial of Service (DRDoS) attacks, underscores a pressing issue. Despite considerable research endeavors, the efficacy of detecting DRDoS attacks remains unsatisfactory. This deficiency calls for the development of pioneering solutions to enhance detection capabilities and fortify cyber defenses against this sophisticated subtype of Distributed Denial of Service (DDoS) attacks. Our study addresses this challenge by utilizing four distinct machine learning algorithms: SVM, DT, RF, and LR, supplemented by PCA. Leveraging the “CIC Bell DNS 2021” dataset, our experiments produce compelling results. Specifically, both DT and RF algorithms exhibit exceptional performance with 100% accuracy and perfect F1 scores. This remarkable performance holds true with or without PCA-based feature reduction, except for dataset 4. Consequently, our research highlights the potential of machine learning in detecting and mitigating DRDoS attacks, offering valuable insights for bolstering cybersecurity measures against evolving threats.