Kavosh: Offering an Analysis Method and the Impact of Normal Network Traffic on Selection and Extraction Based on the Minkowski Distance

The growing spread of botnet threats and the development of new platforms for deploying botnets such as the Internet of Things urges the need for confrontation. Research in the field of botnet detection based on machine learning methods, shows that these methods have the necessary efficiency for botnet detection. In this paper, normal and botnet traffic are analyzed by the proposed method based on the Minkowski distance vector. The results of the article show that normal traffic flow affects the feature selection and extraction stage by changing the importance of features. This method scores the features based on near bot-bot behavioral vectors and far bot-normal behavioral vectors. The results of these experiments on ten sets of normal data and three sets of bot data showed that the score of a feature increases or decreases by more than 50% in environments with various normal traffic.