Threat Classification in Streaming Systems

The system performs preliminary actions by pre-computing threat indicators and maintaining updated threat intelligence databases before malware infections occur. Threat classification models are trained in advance on known malware patterns, enabling rapid matching against new samples without performing exhaustive analysis from scratch, thus reducing detection time while maintaining security coverage.

The patent introduces intermediary components including a threat classification module that acts as a mediator between raw event data and security responses. This intermediary layer processes and classifies events using learned patterns, filtering out benign activities and prioritizing genuine threats, thereby reducing detection time without compromising security reliability.

The system applies local quality by differentiating the level of analysis based on event characteristics and threat probability. High-confidence events receive minimal processing, while low-confidence or novel events trigger comprehensive analysis. This selective deep-dive approach maintains detection accuracy for critical threats while significantly reducing overall computational resource consumption compared to uniform comprehensive analysis.

The patent implements partial action by applying comprehensive security analysis only to a subset of events that meet certain criteria (e.g., novel patterns, high risk scores, or specific file types). The majority of routine events are processed using lighter-weight classification methods, achieving sufficient detection accuracy for critical cases while optimizing resource utilization.

The system incorporates feedback mechanisms where detected threats and false positives are continuously fed back into the threat classification model for retraining. This feedback loop enables the system to learn from past decisions, refine its patterns recognition, and adjust sensitivity thresholds dynamically, thereby maintaining high malware detection rates while progressively reducing false positive alerts through iterative improvement.

The patent applies parameter changes by dynamically adjusting detection thresholds and sensitivity levels based on contextual information and evolving threat landscapes. The system can shift between conservative and aggressive detection modes, adapting parameters such as confidence thresholds, pattern matching strictness, and risk score cutoffs to balance detection rate and false positive reduction in different operational contexts.