Cross-Source Alert Prioritization Using Severity Normalization
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Solution Overview
Problem
Cybersecurity tools generate alerts with varying severity criteria, making direct comparison and prioritization across different sources ineffective, leading to inaccurate and resource-intensive threat management.
Innovation Solution
A machine learning-based alert prioritization system that clusters alerts, normalizes severity, and trains a prioritization model using user inputs to prioritize and automate remediation, reducing the need for explicit prioritization procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cybersecurity tools are used to generate alerts, then threat detection coverage is improved, but alert prioritization accuracy deteriorates due to varying severity criteria across different sources
Solution Approach 1:
The system transforms alerts from multiple sources by normalizing their severity parameters into a unified scale. Each alert's severity is converted to a standardized metric that enables direct comparison across different cybersecurity tools, resolving the inconsistency in severity criteria while preserving the diverse detection capabilities of multiple tools.
Solution Approach 2:
The system introduces a machine learning model as an intermediary between raw alerts from multiple sources and the final prioritization output. This model learns the relationship between alerts from different tools and their actual threat levels, enabling accurate cross-source prioritization by mediating the translation of heterogeneous severity criteria into a unified assessment.
2Measurement precision
If all cybersecurity alerts are manually prioritized, then prioritization accuracy is improved, but resource consumption and time loss increase significantly
Solution Approach 1:
The system enables automated self-service prioritization where the machine learning model independently assesses and ranks alerts without requiring manual intervention for each alert. The model learns from historical data and user feedback to autonomously perform prioritization, dramatically reducing resource consumption while maintaining high accuracy through continuous learning and adaptation.
Solution Approach 2:
The system incorporates feedback mechanisms where user corrections and prioritization decisions are fed back into the machine learning model to continuously improve its accuracy. This feedback loop allows the automated system to learn from human expertise over time, progressively enhancing prioritization precision while maintaining operational efficiency without requiring sustained manual effort.
3Stability of the object's composition
If explicit prioritization procedures are implemented across multiple tools, then prioritization consistency is improved, but system complexity increases
Solution Approach 1:
The system implements a universal machine learning model that serves as a single prioritization engine for alerts from multiple different cybersecurity tools. This multi-functional approach consolidates what would otherwise require separate prioritization procedures for each tool into one unified system, achieving consistency across sources while minimizing the addition of complex tool-specific infrastructure.
Data Source
AI summary
A system and method for machine learning training. A method includes clustering cybersecurity alerts from different alert sources into clusters based on features distances between alerts. A subset of the cybersecurity alerts is selected from each cluster based on the cluster center of the cluster. Each subset is presented via a user device in order to obtain a set of user inputs with respect to each presented subset. A training set is created based on the user inputs with respect to each presented subset. The training set includes the cybersecurity alerts and corresponding training prioritizations. A prioritization machine learning model is trained to prioritize cybersecurity alerts from the plurality of alert sources using the training set.