Real-Time Cyber Threat Detection via Behavioral Analytics

The patent replaces traditional rule-based and signature-based security mechanisms with machine learning models that automatically learn and adapt to detect cyber threats. The system uses trained models to analyze entity behavior patterns, substituting manual security configuration with automated intelligent detection, thereby improving reliability while managing complexity through algorithmic approaches.

The system dynamically adjusts detection parameters and thresholds based on learned behavior patterns from training data. By changing the parameters of the detection algorithm adaptively rather than using fixed thresholds, the system improves its ability to detect novel threats while maintaining operational flexibility, resolving the contradiction between detection capability and system complexity.

The system performs preliminary training of machine learning models offline using historical data before deployment. By pre-processing and training models in advance, the actual real-time detection requires only inference operations rather than full model training, significantly reducing processing time while maintaining high detection precision through pre-optimized models.

The detection process is segmented into distinct phases: offline model training, real-time feature extraction, and anomaly scoring. By dividing the computationally intensive training phase from the time-critical detection phase, the system achieves high precision through thorough model training while maintaining fast response times during actual threat detection operations.