DDOS detection using machine learning model

Abstract

The detection of anomaly traffic has become one of the principal directions in the field of network security intending to identify the attacks based on the specific deviations of the captured traffic. The cybercrime rate is increasing, capabilities of the cyber terrorists and hackers are growing at a higher rate. Today there is a requirement for the innovation and exploration for the mitigation of DDoS attacks. One of the most popular attacks in different layers of the network is Distributed Denial of Service (DDoS) a malicious try to interrupt regular traffic of a directed server, service, or network by irresistible to the target of its nearby infrastructure with anomalous flood traffic to the legitimate servers. An attacker usually targets for gaining access to virtual things like servers, applications, networks and sometimes targets particular transactions in an application. The detection of anomalous network traffic is one of the main challenging problems. which can harm a legitimate user very immensely. In simple language a huge number of false packets from different servers or different systems or software or bots etc sent by the hacker to make a traffic jam on the server. These false packets consist of the same features as the original, which is around 41. These Feature values are different from the original features value which are made by hackers or hacking tools. These false packets are sent to the server in a huge number at a time to make the server busy. Server get busy to responding those false packets. Legitimate users are unable to access the server. Our dataset name is KDDCUP99 which is available on the internet. To detect this type of attack basic Machine Learning Models are good to go. Our main aim in this paper is to make a combined machine Learning model using less features to get a higher accuracy than the previous paper model. Here we have been used Three Classification Machine Learning Algorithm KNN, Random Forest and Naive Biase.