Analysis of Feature Selection Methods for P2P Botnet Detection

Abstract

Botnets are one of the major threats today and one of the main reasons for this is its capability to hide in the network. It is not easy to detect Botmaster, the one who controlled botnets from a far end. There are different technologies and algorithms that are used for the detection of a botnet in a network. Some of the prominent techniques are based on machine learning algorithms. Machine learning have been proven in the past that they are the best in the business and also the leading techniques to detect botnet. In order to implement machine learning algorithms, the most important task is to analyze the dataset very well before using it. Feature selection techniques help in doing this. With the help of different feature selection techniques, we can find out the best criteria for the detection of a botnet. In a particular dataset, there are different numbers and types of features are present, and all these features don’t contribute equally to the detection of the botnet. We need to find out the important features which will be more useful in building a botnet detection model. Many algorithms have been used in the past for botnet detection but most of them have used the different feature selection methods for different datasets. In this paper, we will be covering different feature selection methods and their analysis on the different botnets. Also, in the end, we will be comparing all these techniques and giving the best for a particular botnet.