Modular Cybersecurity Engine for Adaptive Credential Detection

The system segments credential detection into multiple specialized engines: a regex-based engine for simple pattern matching, a machine learning engine for semantic understanding, and a knowledge graph engine for contextual analysis. Each engine handles specific detection scenarios, allowing the system to maintain implementation simplicity for basic cases while achieving comprehensive detection capability through coordinated multi-engine operation.

The system implements a universal cybersecurity platform that performs multiple functions through a single integrated architecture. The modular engine can switch between different detection modes (regex, machine learning, knowledge graph) and can be configured for various threat scenarios (credential scanning, malware detection, anomaly analysis), providing comprehensive threat coverage without requiring separate specialized tools for each function.

The system implements dynamic credential detection that adapts processing methods based on the situation. For known, standardized credential formats, the system uses fast static regex patterns. For novel or complex credential formats, it dynamically switches to machine learning-based semantic analysis that can learn and adapt to new formats. The knowledge graph component further enhances adaptability by inferring credential types from contextual relationships, allowing the system to maintain high processing speed for common cases while achieving flexibility for new formats.