A Survey of AI Methods for Detection of DDoS Attacks on Networks
Anthony Obogo Otiko *
Department of Computer Science, University of Cross River State, Calabar, Nigeria.
Emmanuel A. Edim
Department of Computer Science, University of Calabar, Nigeria.
Gabriel Akibi Iyang
Department of Computer Science, University of Cross River State, Calabar, Nigeria.
Emmanuel Oyo-Ita
Department of Computer Science, University of Cross River State, Calabar, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This survey explores various artificial intelligence (AI) methods for detecting Distributed Denial of Service (DDoS) attacks on networks. It classifies these approaches into machine learning, deep learning, and other AI-based techniques, providing a comprehensive overview of current advancements in the field. Numerous research studies in the field of machine learning have evaluated DDoS attack detection performance using various datasets and techniques. Some noteworthy results are the supremacy of the J48 algorithm in SDN networks and the efficacy of the AdaBoost and Gradient Boost classifiers. In other investigations, Random Forest, Support Vector Machine, and Naive Bayes also showed excellent accuracy rates, up to 99.7%. To improve DDoS detection, deep learning techniques introduced autoencoders, hybrid models, and recurrent neural networks. These models achieved accuracy rates as high as 99.99%, frequently outperforming more conventional machine learning techniques. Enhanced detection rates were achieved by the utilization of a varied dataset in conjunction with deep-stacked autoencoders. Artificial intelligence methods such as Fuzzy Logic, Artificial Bee Colony, Ant Colony Optimization, and Whale Optimization Algorithm were used to identify DDoS assaults. These methods demonstrated high accuracy rates, efficient detection of various attack types, and improvements in reducing false positives; the integration of these techniques into intrusion detection systems offers a strong defense against dynamic DDoS threats. The overall survey highlights the effectiveness of AI techniques in DDoS attack detection across various methodologies.
Keywords: Algorithm, artificial intelligence, denial of service, distributed denial of service, malicious traffic