Effective DDoS Detection through Innovative Algorithmic Approaches in Machine Learning

A Distributed Denial of Service (DDoS) attack disrupts online services by overwhelming them with unauthorized traffic, posing significant network security risks. Traditional detection techniques struggle with the evolving nature of these attacks. This study proposes an innovative machine learning approach to accurately identify and prevent DDoS attacks, enhancing detection speed while minimizing false alarms. The optimized model involves network traffic preprocessing, training on large datasets, and performance verification under various attack scenarios. The research reveals the proposed algorithm detects DDoS attacks with 98% accuracy and an average response time of 2 seconds, reducing false positives by 40% compared to conventional systems. Additionally, the algorithm is scalable, accommodating new attack forms and diverse networks. Implementing these models improves understanding of attack patterns, leading to better coping mechanisms over time. The developed machine learning algorithm provides an effective, adaptive solution for DDoS detection.