Approach to Detecting Malicious Bots in the Vkontakte Social Network and Assessing Their Parameters

Abstract

The emergence of new varieties of bots in social networks and the improvement of their capabilities to imitate the natural behavior of real users represent a significant problem in the field of protection of social networks and online communities. This paper proposes a new approach to detecting and assessing the parameters of bots within the social network «VKontakte». The basis of the proposed approach is the creation of datasets using the method of «controlled purchase» of bots, which allows one to assess bots’ characteristics such as price, quality, and speed of action of bots, and using the Turing Test to assess how much users trust bots. In combination with traditional machine learning methods and features extracted from interaction graphs, text messages, and statistical distributions, it becomes possible to not only detect bots accurately but also predict their characteristics. This paper demonstrates that the trained machine learning model, based on the proposed approach, is robust to imbalanced data and can identify most types of bots as it has only a minor correlation with their main characteristics. The proposed approach can be used within the choice of countermeasures for the protection of social networks and for historical analysis, which allows not only to confirm the presence of bots but also to characterize the specifics of the attack.