Detection of DDoS Attack Using Naive Bayes Classifier
Md. Amir Khusru Akhtar, Mohit Kumar · Advances in information security, privacy, and ethics book series · 2020
Naive Bayes classifiers are a set of categorization techniques based on Bayes' theorem. It is a collection of algorithms where all these algorithms share a common principle. This chapter presents the detection of DDos attack using scoreboard dataset. The dataset is separated into two parts, that is, feature vector and the reaction vector. Feature vector contains all the rows of dataset in which each vector consists of the value of dependent features such as IP address, port, counter, flag, syncnt, no. of packets, etc. The reaction vector contains the value of class variable (prediction or output) for each row. Result shows the effectiveness of the model in preventing DDoS attack by classifying request.