Implementation of QOS in SDN and Distributed Networks for mitigation of DDOS based attacks using Machine Learning

Distributed Denial Service of Service (DDoS) is very sophisticated attack which brute-force packet jamming to a network to render it useless, if done with large number of nodes. It can be easily countered by a number of techniques such as load balancing, rate limiting and many newer intelligent systems techniques but attackers are continuously developing new techniques to circumvent traditional defense mechanisms. The Sophistication of the attack is determined by current defenses in place and duration of attack. The use of DDoS attack is done on distributed Network traffic by an attacker which might send more traffic than a network card can handle or overwhelm an application with more requests than it can process which might led to not able to add legitimate user to the application With coming of Web 3.0 the DDoS attack grown exponentially , So we are proposing a Machine Learning based traffic filtering system by which we can determine the legitimate user entering to our application resources with encrypted HTTPS traffic. Pirated and duplicate IP events filtering will lead to clean resources inside Web3.0 and gaming environment which will improve the efficiency, latency and performance of game and similar resources. We will use QoS parameters in SDN parameters to define the network governance and tuning mechanism so that application traffic is correctly routed and prioritized. One of the major reasons to propose this research is because right now the major rDDoS attacks shifted from L3/L4 Network Layer to L7 Application layer causing major increase in attack size ratio.