Systematic Literature Review on Distributed Denial of Service Attack

Abstract

Distributed Denial of Service (DDoS) attacks are one of the more sophisticated threats that have been targeting the internet and systems in recent years. Traditional machine learning-based intrusion detection systems (IDSs) frequently do not detect these attacks effectively when they are trained on unbalanced datasets. This work provides a system literature review (SLR) on Distributed Denial of Service (DDoS) attack detection, by presenting a detailed assessment of the approaches and methodologies taken throughout the nine years, emphasizing machine learning and deep learning techniques. The review examines various approaches, including token embedding for feature extraction, transformer-based models, and hybrid detection techniques. Despite improvements, the study highlights ongoing challenges, such as computational complexity and the need for enhanced solutions like blockchain-based detection systems. Open research gaps and future directions, including the refinement of detection algorithms for evolving DDoS tactics, are also discussed, offering a comprehensive resource for researchers aiming to improve DDoS mitigation