A Fuzzy Logic based feature engineering approach for Botnet detection using ANN

Abstract

In recent years, Botnet has become one of the most dreadful type of malicious entity. Because of the hidden and carrying capacity of Botnet, the detection task has become a real challenge. Different methodologies have been applied for finding the source of Botnet at an early stage. Machine Learning and Deep Learning have greatly impacted these Botnet detection methodologies. But still, it is a difficult task because of the lesser number of features available in the Botnet datasets. In this paper, we have proposed a Fuzzy Logic based feature engineering method. The proposed method first identifies the fuzzy elements in the dataset and then generates fuzzy sets. The features generated using this method is used by an Artificial Neural Network for classification of Botnet. To train and evaluate the ANN model, we have used the CTU-13 dataset. The proposed feature engineering method and Botnet classification method has performed well with an accuracy of 99.94%. Still this method needs to be tested on different datasets. In future, new fuzzy rules can also be made to generate a new set of features as well as these rules can be used to generate features from other datasets.