Disruptor Validation Engine for ML-Based Threat Alert Quality Grading
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Solution Overview
Problem
Conventional disruptor validation methods rely heavily on manual processes, which are labor-intensive, time-consuming, and prone to errors, leading to inconsistent and inaccurate classification of disruption alerts, thereby undermining the reliability of security systems in detecting cyber threats.
Innovation Solution
A disruptor validation engine that utilizes machine learning models to automatically assess the quality and accuracy of disruptors by generating probability grades for disruption alerts, classifying them into valid, invalid, or unknown categories, and calculating a signal-to-noise ratio (SNR) estimation to determine the disruptor's readiness for deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used for disruptor validation, then human expertise can be applied to assess disruption alerts, but the process becomes labor-intensive, time-consuming, and prone to errors
Solution Approach 1:
The patent replaces manual human review processes with an automated machine learning model that analyzes disruption alerts. The system uses trained ML models to generate probability grades and classifications automatically, eliminating the need for manual human assessment while maintaining or improving classification accuracy and significantly reducing validation time.
Solution Approach 2:
The disruptor validation engine performs self-validation through automated ML models that independently assess disruption alerts without requiring external human intervention. The system uses historical data and trained models to autonomously generate probability grades, determine classifications, and calculate quality metrics, enabling the system to validate itself continuously.
2Reliability
If manual processes are used for disruptor validation, then human judgment can be applied, but the process becomes inconsistent and inaccurate
Solution Approach 1:
The patent transforms subjective human judgment into objective quantitative parameters through probability grades generated by ML models. The system converts qualitative assessments into measurable numerical values (probability scores, classification categories, quality grades) that provide consistent and reproducible results across different validations.
Solution Approach 2:
The system implements continuous feedback loops where disruption alerts are analyzed, probability grades are generated, classifications are determined, and quality metrics are calculated. This feedback mechanism allows the ML models to learn from historical data and improve validation consistency over time, while the automated process eliminates human variability.
3Productivity
If automated machine learning models are used, then validation speed and consistency improve, but the system complexity increases
Solution Approach 1:
The disruptor validation engine is designed as a multi-functional system that performs multiple validation tasks through a single integrated platform. The ML models handle probability grade generation, classification determination, quality grade calculation, and SNR estimation simultaneously, improving productivity while consolidating complexity into a unified system rather than multiple separate tools.
Solution Approach 2:
The system performs preliminary training of ML models using historical disruption alert data before deployment. This preliminary action prepares the models in advance, allowing them to process new alerts quickly and consistently once deployed, thereby improving validation throughput while the complexity is managed during the offline training phase rather than during real-time operation.
Data Source
AI summary
Systems and methods herein provide a disruptor validation engine and its related functions. In an aspect, a disruptor validation engine may determine disruption alerts issued by one or more disruptors, where a disruption alert indicates potential malicious activity within a tenant environment. Responsive to receiving the disruption alerts, the disruptor validation engine may generate a probability grade for a respective disruption alert. The probability grade may indicate a likelihood that the potential malicious activity triggering the disruption alert is actually malicious. The disruptor validation engine may then determine a disruption classification for the disruption alert based on a respective probability grade. Based on the disruption classification, the disruptor validation engine may generate a quality grade for the one or more disruptors indicating the validity and quality of the respective disruptor.