DDoS Attack Detection using ML

Abstract

Abstract: DDoS attacks are an attempt to prevent the service from being unavailable by overloading the server with malicious traffic. In the past few years, distributed denial of service attacks is becoming the most difficult and burdensome problem. The number and magnitude of attacks have increased from few megabytes of data to 100s of terabytes of data these days. As there are different attack patterns or new types of attacks, it is difficult to detect such attacks effectively. New techniques for generating and mitigating distributed denial of service attacks have been developed in the present paper, which demonstrate that they are far superior to those currently used. In addition, in order to carry out a thorough investigation of the challenges presented by distributed denial of service attacks, we classify DDoS attack methods and techniques used for their detection. We're comparing the attack module to a few other tools out there.