Machine Learning-Based Anomaly Detection for Access Log Data

Organization A recognized limitations in systematically managing sensitive data, including personal information. At that time, their security system could only detect misuse cases defined by security personnel (rule-based), making it incapable of identifying new patterns or abnormal patterns.

With the advancement of various technologies and the increasing incidents of personal data breaches, the need for robust tools and capabilities to protect citizens' personal information has become more crucial than ever.

To build a comprehensive information security monitoring system for personal data protection, Organization A developed a step-by-step approach, including user log data-based pattern analysis/diagnostic modules and distance-based anomaly detection technology-based security management systems. Additionally, they proposed improvements to the existing security system and policies through Machine Learning-based Big Data analysis methods.

Detecting abnormal patterns using log data enhances system security and stability. Machine learning models allow for flexible expansion of the security system while comprehensively analyzing and linking key aspects of security management. This prevents the leakage and misuse of sensitive personal information, as well as addresses security issues related to unauthorized devices and account usage.