ML-Based Security Event Detection in Code

The patent replaces manual mechanical code review with an automated machine learning-based analysis system. The ML model automatically scans code, identifies security events, and generates logging statements without human intervention, thereby eliminating time loss while improving logging coverage reliability.

The system enables code to self-identify security events and self-generate appropriate logging statements through automated analysis. The ML model processes code independently, detecting security-relevant operations and automatically creating logging infrastructure without requiring programmer action.

The patent extracts the complexity of security logging implementation from the main codebase by using an external ML analysis system. The ML model handles the complex task of identifying security events and generating logging statements separately, leaving the main code clean and simple while still achieving comprehensive security monitoring.

The ML-based analysis system acts as an intermediary between the code and security logging requirements. Instead of directly complicating the code with logging statements, the intermediary automatically analyzes and generates appropriate logging infrastructure, simplifying the overall system while improving detection capability.

The patent applies logging statements selectively based on local code characteristics. The ML model analyzes each code segment and adds logging only where security events occur, rather than uniformly throughout the codebase. This localized approach maintains complete audit trails while preserving code readability and maintainability in non-security-critical sections.

Instead of adding logging statements to all code by default, the system inverts the approach by using ML to identify only the specific locations where logging is necessary. This inversion reduces unnecessary logging overhead while ensuring complete coverage of security-relevant operations, thereby maintaining code quality.