Alert Classification Model for Cybersecurity Triage
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Solution Overview
Problem
Cybersecurity analysts face alert fatigue due to the high volume of alerts, making it difficult to identify the most severe and relevant threats quickly and accurately.
Innovation Solution
A system that uses a machine learning model to automatically classify and triage alerts by creating a feature vector based on static and dynamic features, allowing for the prediction of actions to be taken on new alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security analysts manually review all alerts to identify cybersecurity attacks, then the accuracy of threat identification is improved, but the time required to respond to alerts increases and analyst fatigue worsens
Solution Approach 1:
The machine learning model performs preliminary triage of alerts by predicting the likelihood of malicious activity before human analysts review them. The system automatically classifies alerts into categories and generates feature vectors that include both static alert information and dynamic trends from similar historical alerts, enabling rapid preliminary assessment that reduces the time analysts need to spend on routine threat identification.
Solution Approach 2:
The machine learning model acts as an intermediary between alert generation and human analyst review. It processes alerts through automated classification and triage, filtering and prioritizing them based on predicted maliciousness. This intermediary layer handles the time-consuming analysis of alert patterns and historical trends, allowing analysts to focus only on high-priority threats that require human intervention.
2Reliability
If security analysts manually review all alerts, then the reliability of security monitoring is improved, but the productivity of the security team decreases due to alert fatigue
Solution Approach 1:
The machine learning model performs self-service triage by automatically analyzing alerts against historical data and generating predictions without human intervention. It continuously learns from historical alerts and updates its classification accuracy, enabling the system to autonomously handle routine alert assessment and reduce the workload on security analysts, thereby improving team productivity while maintaining monitoring reliability.
Solution Approach 2:
The system incorporates feedback from historical alert outcomes into the machine learning model's training. By analyzing the results of previous alert triage decisions and analyst actions, the model continuously refines its prediction accuracy. This feedback mechanism ensures that the automated triage becomes increasingly reliable over time, allowing the security team to maintain high productivity without sacrificing monitoring reliability.
3Quantity of substance
If the volume of alerts increases, then the coverage of security monitoring is improved, but the difficulty of identifying severe threats quickly increases
Solution Approach 1:
The machine learning model segments alerts into different categories and priority levels based on their predicted severity and characteristics. By dividing the alert stream into manageable segments according to threat level, the system enables security analysts to quickly identify and focus on severe threats while the automated system handles the bulk of routine alerts, maintaining effective threat detection even as alert volume increases.
Solution Approach 2:
The system changes the parameters used for alert assessment by incorporating dynamic features that capture temporal trends and patterns from historical data. The model adjusts its evaluation parameters based on the timing, frequency, and pattern of similar past alerts, enabling it to quickly identify severe threats even in high-volume alert environments by detecting anomalies in the temporal distribution and behavior patterns of alerts.
Data Source
AI summary
Systems and methods are provided for automatically classifying and triaging alerts. A probabilistic classification machine learning model is trained using historical alerts and the actions taken for each of the historical alerts. When a new alert is received, it is categorized. A feature vector is created using information associated with the alert and count information associated with other recently received alerts that have the same category or at least one common entity with the alert. Count information for attributes and combinations of attributes for recently received alerts is maintained in a time series database. The machine learning model is applied to the feature vector to determine a suggested action. Once an analyst action is taken with respect to the alert, the count information in the time series database is updated.