IMPLICIT THREATS IDENTIFICATION BASED ON ANALYSIS OF USER ACTIVITY ON THE INTERNET SPACE
The article is devoted to the problem of identifying implicit information threats of a user's search activity in the internet space based on an analysis of his activity in the course of this interaction. The use of knowledge stored in the Internet space for the implementation of criminal intentions poses a threat to the whole society. Identifying malicious intent in the users' actions of the global information network is not always a trivial task. The proven technologies for analyzing the context of user interests fail in the case of cautious and competent actions of attackers who do not explicitly demonstrate the goal they are pursuing. The paper analyzes the threats associated with certain scenarios for the implementation of search procedures that manifest themselves in search activities. Criteria of inefficient and effective search scenarios estimation are described. Among the signs indicating the possibility of a threat, the following main ones are highlighted: avoiding solving the problem in aimless navigation or attractive resources, superficial search, lack of meaningful immersion in solving the search problem, and chaotic actions during the search. To determine the presence of adverse signs, a system of indicators is built. The features of an effective scenario for organizing a search in the Internet space are formulated, options for the presence of implicit threats for a similar situation are described.An approach for identification the described threats is presented taking into account the specified criteria for evaluating various scenarios of user behavior in the global information space. A machine learning algorithm has been developed to identify problem scenarios by comparing with key behavioral patterns. The software implementation of the subsystem for identifying information threats has been created, experimental studies have been conducted to confirm the effectiveness of the subsystem. Experimental studies were carried out on the basis of processing open data from social networks, as well as using analysis of user search activity in the university corporate information environment.