Cybersecurity Alert Clustering With Weighted TSF-IDF Prioritization
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Solution Overview
Problem
Current cybersecurity systems generate a high volume of alerts that overwhelm security analysts, making it difficult to prioritize and address threats effectively due to limited resources, leading to undetected and invasive threats.
Innovation Solution
Implementing a weighted Time Series Frequency-Inverse Document Frequency (TSF-IDF) algorithm for clustering and scoring cybersecurity alerts, combined with Bayesian estimation for confidence scoring, to prioritize clusters based on frequency, uniqueness, and accuracy, using a continuous feedback loop for adaptive improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If organizations expand cybersecurity monitoring efforts to detect more threats, then threat detection coverage is improved, but alert volume increases overwhelming analysts
Solution Approach 1:
The patent segments the large volume of alerts into clusters based on similarity metrics and grouping criteria. By dividing alerts into meaningful clusters rather than treating them as individual items, the system reduces the overwhelming quantity of alerts while preserving comprehensive threat detection coverage across all segmented groups.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that evaluates and ranks alert clusters based on multiple factors including novelty, confidence, and severity. This intermediary layer between raw alerts and analyst review filters and prioritizes information, allowing analysts to focus on high-value clusters while automated systems handle lower-priority items.
2Measurement precision
If analysts review all alerts thoroughly to ensure accurate threat detection, then detection accuracy is improved, but resource consumption increases beyond available capacity
Solution Approach 1:
The patent applies local quality by providing different levels of analysis and scrutiny to different alert clusters based on their characteristics. High-scoring clusters that exhibit novel or suspicious patterns receive more detailed analyst attention, while routine or low-risk clusters receive automated processing. This differentiated approach maintains high detection accuracy for critical threats while preserving analyst productivity through selective deep analysis.
3Reliability
If the system processes and analyzes every alert to maintain security, then security reliability is improved, but processing time and computational resources exceed available capacity
Solution Approach 1:
The patent performs preliminary actions by pre-computing similarity metrics, confidence scores, and clustering assignments for alerts before they reach the analyst review stage. This advance processing and pre-filtering reduces the computational burden during critical response times, maintaining security reliability through thorough preliminary analysis while minimizing real-time processing delays.
Solution Approach 2:
The patent implements feedback mechanisms where outcomes from analyst reviews and incident resolutions are fed back into the system to refine clustering algorithms and scoring models. This continuous feedback loop improves the system's ability to automatically identify and prioritize high-value alerts over time, reducing the need for exhaustive manual review while maintaining or improving security reliability.
Data Source
AI summary
Systems and methods for intelligently clustering alerts and applying a multi-stage scoring approach to prioritize and effectively address cybersecurity alerts are disclosed. The multi-stage scoring approach may involve applying Time Series Frequency-Inverse Document Frequency (TSF-IDF) Scores that represent a novelty of a cluster and Confidence Scores that represent a measure of accuracy based on prior performance including true positives and other indicia of accuracy.