SEMI-SUPERVISED MACHINE LEARNING APPROACH FOR DDOS DETECTION

Distributed denial of service (DDoS) attacks are a major threat to any network-based service provider. The ability of an attacker to harness the power of a lot of compromised devices to launch an attack makes it even more complex to handle. This complexity can increase even more when several attackers coordinate to launch an attack on one victim. Moreover, attackers these days do not need to be highly skilled to perpetrate an attack. Tools for orchestrating an attack can easily be found online and require little to no knowledge about attack scripts to initiate an attack. The purpose of this paper is to detect and mitigate known and unknown DDoS attacks in real time environments. Identify high volume of genuine traffic as genuine without being dropped. Prevent DDoS attacking (forged) packets from reaching the target while allowing genuine packets to get through. A DDoS attack slows or halts communications between devices as well as the victim machine itself. It introduces loss of Internet services like email, online applications or programme performance. We apply an automatic characteristic selection algorithm primarily based on N-gram sequence to obtain meaningful capabilities from the semantics of site visitors flows. DDoS attacks are the perfect planned attacks with the aim to stop the legitimate users from accessing the system or the service by consuming the bandwidth or by making the system or service unavailable. The attackers do not attack to steal or access any information but they decline the performance of the network and the system