Ddos Attack Prediction Using Voting Classifer

Abstract

DDOS attack causes the distortion in performance of wireless sensor network. Sensor energy decays quickly by the applications of the DDOS attack. Distribute denial of service causes the performance delays by deadlock. Deadlock causes the resource block and hence
no process or client can access the resources over the network. This paper proposed an ensemble of algorithms to detect DDOS attack with high classification accuracy. For the detection process, we will use KNN, naïve bayes and support vector machine. The ensemble
of algorithm known as voting classifier is proposed that takes all the algorithms and produce the result according to effective properties of each ensembled algorithm. This result of the ensemble-based approach in terms of classification accuracy is 99% which is
significantly higher as compared to individual approaches.