Self-Learning Cybersecurity Alert Engine for SOC Overload
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cyber-security operations centers (SOCs) face overwhelming volumes of cyber-security alerts, including duplicates, false positives, and low-priority alerts, which overwhelm analysts and hinder effective threat mitigation.
Innovation Solution
An automated analyst alerting system uses a predictive machine learning model to analyze and prioritize cyber-security alerts, generating classifications and recommended actions, and implements a self-learning feedback loop to improve alert analysis and mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SOC analysts manually analyze all cyber-security alerts, then thorough threat detection is achieved, but analyst workload and response time become unmanageable
Solution Approach 1:
The patent introduces an automated alert analysis system as an intermediary between cyber-security alerts and human analysts. This system uses machine learning models to pre-analyze alerts, generate classifications, and prioritize threats before presenting them to analysts, thereby filtering the overwhelming volume of alerts while maintaining thorough detection capabilities
Solution Approach 2:
The system enables alerts to be self-analyzed through automated classification and prioritization mechanisms. The machine learning models automatically process alert data, generate classifications, and rank threats by severity, allowing the system to serve itself in the initial analysis phase without requiring human intervention for every alert
2Reliability
If all cyber-security alerts are presented to analysts, then comprehensive threat coverage is maintained, but alert overload reduces effective response capability
Solution Approach 1:
The patent applies different processing qualities to different alerts based on their characteristics. High-priority alerts receive detailed automated analysis and are prominently presented to analysts, while low-priority alerts are filtered or summarized. This local differentiation in analysis depth and presentation style maintains comprehensive coverage while improving analyst efficiency
Solution Approach 2:
The system performs partial analysis on all alerts through automated classification, but focuses excessive (detailed) analysis only on high-priority alerts that require analyst attention. This selective depth of analysis maintains comprehensive threat coverage while preventing alert overload by not presenting all alerts with the same level of detail
3Productivity
If traditional alert filtering is used to reduce volume, then analyst workload is reduced, but false positives and duplicates are not effectively distinguished
Solution Approach 1:
The patent replaces traditional mechanical filtering rules with machine learning-based classification systems. Instead of using predetermined filters that may misclassify alerts, the system uses trained models that learn from data to accurately distinguish false positives from genuine threats, improving classification precision while maintaining productivity
Solution Approach 2:
The system dynamically changes classification parameters based on learned patterns from alert data. The machine learning models adjust classification thresholds and criteria based on the specific characteristics of each alert and historical data, enabling accurate differentiation of false positives from real threats while maintaining high processing capacity
Data Source
AI summary
Techniques for performing cyber-security alert analysis and prioritization according to machine learning employing a predictive model to implement a self-learning feedback loop. The system implements a method generating the predictive model associated with alert classifications and/or actions which automatically generated, or manually selected by cyber-security analysts. The predictive model is used to determine a priority for display to the cyber-security analyst and to obtain the input of the cyber-security analyst to improve the predictive model. Thereby the method implements a self-learning feedback loop to receive cyber-security alerts and mitigate the cyberthreats represented in the cybersecurity alerts.