Use of Machine Learning for Web Denial-of-Service Attacks: A Multivocal Literature Review

Denial-of-service (DoS) attacks conducted on online systems cause the targeted resources to become inoperative. This is caused by the abnormal traffic intentionally directed toward the system resources by the attacker(s). Several techniques can be used to detect and mitigate such attacks, and machine learning (ML) is one of them. Herein, the literature on ML techniques used to address DDoS attacks is systematically reviewed. Five search engines are used to identify relevant studies, the results are filtered based on specific selection criteria, and 48 studies are shortlisted and analyzed. Considerable variations are observed in the literature with respect to the datasets used for training ML models, with more than 20 datasets being used. To conduct performance evaluations, most studies have employed the accuracy metric. More than 30 modeling algorithms have been used to build the ML models. The outcomes of this review reveal the state of the literature and the manner in which the problem of DDoS attacks has been dealt with. Our results can guide future studies toward more constructive methods than the existing ones to address this problem.