Leveraging Machine Learning and Deep Learning Technologies for Predicting Distributed Denial of Service Attacks: A Systematic Review Analysis

Technologies, especially Fourth Industrial Revolution Technologies (4thIRTs) like Big Data Analytics (BDA), Artificial Intelligence (AI), and Cloud Computing (CC), among others, have led to exponential growth in intrusions and assaults across Internet-based technologies. One of the fatal dangers rising is the distributed denial of service (DDoS) assault that may shut down Internet-based systems and applications in no time. The attackers are changing their skills frequently and consequently avoiding the existing detection mechanisms. Since the number of files created and stored has expanded manifolds, the standard detection systems are not suited for identifying modern DDoS attacks. With the emergence of network-based computing technologies like cloud computing, fog computing, and IoT (Internet of Things), the context of digitizing confidential data over the network is being adopted by various organizations where the security of that sensitive data is considered a major concern. Over the past decade, there has been massive growth in the usage of the internet, along with technological advancements that demand the development of efficient security algorithms that can withstand various patterns of security breaches. The work systematically evaluates the prominent literature, specifically in deep learning, to identify DDoS using machine learning techniques