A Multi-Source Behavioral Analytics Approach for Real-Time Insider Threat Detection

Insider threats are one of the most significant challenges that modern organizations face today, as any malicious actions are executed by users with legal access privileges. Traditional security solutions cannot handle insider threats, as there is a need to perform continuous monitoring of user actions. Currently, the majority of available approaches rely on either a rule-based approach or use a single source of data, which negatively affects the efficiency of detecting attacks. This paper presents a novel approach based on the utilization of multiple sources of data for analyzing user behavior in order to detect potential insider threats. The system utilizes login history, file access history, and other relevant information to identify user behavior patterns and apply appropriate rules and machine learning algorithms to detect any deviations from standard patterns. To ensure optimal results, the proposed method combines a rule-based approach with machine learning algorithms to detect anomalies in user behavior. To make decisions about specific actions, the system also implements a dynamic risk scoring algorithm, which can be used to evaluate user behavior and assign scores for each specific action.