Application of the Negative Selection Algorithm to Detect Distributed Denial of Service Attacks

The high demand for information technology services during the COVID-19 pandemic has amplified users’ susceptibility to internet security issues. Among these concerns, distributed denial of service (DDoS) attacks have emerged as a prominent threat. These attacks exploit botnets to overwhelm servers with malicious traffic, severely impacting their functionality. This paper investigates an approach utilizing the Negative Selection Algorithm, a bio-inspired computational algorithm, for the detection of DDoS attacks. Through empirical evaluation, this study assesses the detection rate of the proposed solution under varying network protocols. The results of this analysis contribute to an understanding of the feasibility and potential benefits of employing bio-inspired algorithms, such as the Negative Selection Algorithm, in fortifying internet security against the evolving landscape of DDoS attacks.