The widespread use of social media platforms has led to the accumulation of vast amounts of stored data, enabling the prediction of rare events based on user interaction analysis. This study presents a method for predicting rare events using graph theory, particularly graphlets. The social network VKontakte, with over 90 million users, serves as the data source. The ORCA algorithm is utilized to identify characteristic graph structures within the data. Throughout the study, user interactions were analyzed to identify precursors of rare events and assess prediction accuracy. The results demonstrate the effectiveness of the proposed method, its potential for threat monitoring, and the possibilities for further refinement of graphlet-based prediction models.

Keywords: social media, security event, event prediction, graph theory, graphlet, interaction analysis, time series analysis, correlation analysis, data processing, anomalous activity

The article proposes a methodology for design an attribute space to detect behavioral anomalies of users in CRM systems. It describes methods for recording actions through integrated trackers that capture user activity, clicks, cursor movements, and keystrokes. The aggregation of this data into feature vectors enables the application of machine learning algorithms to detect anomalies and enhance information security in the CRM system.

Keywords: information security, CRM system, behavioral analysis, anomaly detection, user identification, behavioral analytics, activity monitoring, digital footprint, insider threats, attribute space