Identification of Implicit Threats Based on Analysis of User Activity in the Internet Space

The article is devoted to the problem of identifying implicit information threats of user’s search activity in the Internet based on the analysis of activity during the interaction process. Application of knowledge stored in the Internet space for the implementation of criminal intentions poses a threat to the whole society. Identifying malicious intents in the users’ actions in the global information network is not always a trivial task. Modern technologies for analyzing the context of users’ interests can fail in terms of cautious and competent actions of malicious users, who do not demonstrate their intentions explicitly. The paper analyzes the threats related to certain scenarios for implementing the search procedures, which are expressed in the search activity. Authors present an approach to classification of the mentioned threats considering the given criteria of estimating different scenarios of the user’s behavior in the global information space. The article describes the developed algorithm of machine learning to identify the problem scenarios by comparing them with the key patterns of behavior. To implement the proposed approach, the authors developed software implementing the subsystem for identifying information threats. The experimental research proves the effectiveness of the developed subsystem.