A Synthesized K-fold approach for Detecting DDoS attack using Machine Learning Solutions

A distributed denial of service attempt is a sort of cyber-attack in which the perpetrator attempts to starve a network/server of services by flooding the network/server with unnecessary requests, rendering it unable to process valid requests. Using the bots devices false requests makes the system busy and unavailable. Since the rate of cyber attacks include ransomware, phishing, and DOS / DDOS attacks etc. increasing becoming more difficult to identify and protect online businesses against them. Researchers are working hard to figure out in defending against these attacks using the machine learning to prevent from causing maximum harm. This paper reveals the various machine learning paradigms for detecting DoS/DDoS threats and also discussed recent years’ big cyber-attacks and their peak traffic. This paper discuses distinct datasets that are widely used by researchers and their accuracy and analyze their approaches used in the detection of DDoS