Abstract

This study presents a robust algorithmic approach to evaluating login security behavior using multi-criteria analysis. By integrating parameters such as login attempts, session duration, and data upload volumes, the study aims to quantify user activity risks and enhance security threat detection. The developed model calculates a Threat Risk Score to evaluate potential threats across diverse user profiles. The proposed methodology facilitates proactive identification of abnormal behaviors, which is critical for real-time cybersecurity operations.  Research Significance: In an era where cybersecurity threats are increasingly sophisticated, identifying risky user behaviors through data-driven analysis is of paramount importance. This research contributes significantly by offering a novel threat evaluation framework based on behavioral parameters. The approach allows organizations to detect potential security breaches early, thereby reducing the attack surface and improving response efficiency.