Machine learning combating DOS and DDOS attacks

In recent years, technology is booming at a breakneck speed as so the need of security. Vulnerabilities in the layers of the OSI model and the networks are paving new ways for intruders and hackers to steal the confidential information. Security attacks such as denial of service (DOS) and distributed denial of service (DDOS) are occurring at a high pace with the help of internet of things as several devices are connected together unprotected in nature and are not monitored for a longer duration. Intruders are initiating more and more complex forms of attacks in order to bypass the known security measures. Application layer is most prone to the DOS and DDOS attacks and intruders targets high profile organisations for launching the attacks. Hypertext transfer protocol (HTTP) is the protocol that is vulnerable to the security attacks launched by the intruders, as high volume of HTTP traffic is handled by the data centres, thus creating a perfect room for launch of DOS and DDOS attack. This paper presents a review of machine learning algorithms that have been developed for the detection and prevention against DOS and DDOS attacks.