DDoS Attack Detection: Strategies, Techniques, and Future Directions

Abstract

Distributed Denial of Service (DDoS) attacks represent one of the most significant threats to network security, capable of causing widespread disruption to digital infrastructures. The potential for extensive damage becomes even more critical when these attacks are executed on a large scale. Numerous research efforts have been dedicated to understanding and mitigating this formidable threat. This study delves into the complex landscape of DDoS attacks, examining a range of strategies proposed for their detection and mitigation. Special attention is given to the exploration of advanced deep learning and machine learning techniques, which have emerged as pivotal in the development of effective defense mechanisms against DDoS attacks. This research offers a comprehensive understanding of the evolving dynamics of DDoS attacks and highlights innovative methodologies, thus contributing to the ongoing discourse on enhancing network security. Additionally, the paper discusses future directions in DDoS detection, aiming to provide a roadmap for researchers and practitioners in the field.