DESIGN AND IMPLEMENTATION OF AN ENHANCED MODEL OF SUPPORT VECTOR MACHINE WITH RADIAL BASIS FUNCTION AND PARTICLE SWARM OPTIMIZATION FOR DETECTION OF DDoS ATTACKS
Résumé
The increasing reliance on networked systems in the Information and Communication Technology (ICT) landscape has made them crucial for managing Critical Infrastructure (CI) and information access. However, this dependence has also made these systems vulnerable to Distributed Denial-of-Service (DDOS) attacks, which disrupt services by flooding target hosts with traffic. Traditional security measures often fall short against these attacks due to their dynamic nature and evolving tactics. To address this challenge, various researchers have explored the potential of Machine Learning (ML). This study proposes a hybrid solution that combines a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel and Particle Swarm Optimization (PSO) for feature selection to enhance DDOS attack detection accuracy. Using the KDD-99 dataset, the SVM-PSO model is trained and evaluated, achieving a superior performance of 99.99% compared to SVM-RBF alone with 99.72%. The findings demonstrate the efficacy of the proposed approach in detecting DDOS attacks, showcasing its potential for bolstering cyber defences. Additionally, comparative analysis with state-of-the-art algorithms highlights the superior accuracy of the SVM-RBF-PSO model, underscoring its effectiveness in mitigating DDOS threats. This study contributes valuable insights into enhancing network security through innovative machine learning techniques, addressing the persistent challenges posed by DDOS attacks.
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