Detection of DDoS Attack Using UCLA Dataset on Different Classifiers - Aggarwal, Aakriti; Gupta, Ankur (International Journal of Computer Sciences and Engineering, 2015)
Distributed denial of service attack have strong Impact on security of internet because these attacks affects the normal functioning causing loss of billions of dollars. DDoS is very harmful to network as it delays the legitimate users from excessing the server. However these networks were well equipped in security yet they were damaged by DDoS attack. In this paper, the proposed system presents both detecting and classifying schemes of DDoS attack using K-NN, SVM and Naïve Bayesian. The algorithms are developed by using various features of attack packets. By studying the incoming and outgoing network traffic and different classifiers are used to analyze these features. The main objective of this paper is to study classifiers on one dataset for DDoS attack.