Identifying Botnets in the Network using Gaussian Mixture Models

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Abstract

Botnets are the primary way of attacking computer networks and are being used to steal information, spy organizations or send spams, by compromising devices connected to the internet. More recently, botnets have also seen themselves being used for financial interests such as mining bitcoins at a large scale. It is a primary threat which is essential to identify in order to defend the interests of users and any type of organization. Unfortunately, public research has often been one step behind the fast adaptation of attackers to detection systems. Our work consists in using unsupervised machine learning techniques unprecedentedly used on such tasks to detect botnets on different scenarios.