Security Alert Classification Feedback for Label Mismatch Detection
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Solution Overview
Problem
Existing security systems over-report security alerts, leading to manual classification errors by human analysts, which can result in misclassification of security incidents, potentially delaying the identification of real threats.
Innovation Solution
A classification model is trained to automatically identify errors in manually-assigned incident characterization labels by comparing user-provided labels with predicted classifications, generating mismatch alerts, and allowing users to update labels, thereby improving the accuracy of incident classification data for security systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classification model is used to automatically identify errors in manually-assigned labels, then the accuracy of incident classification is improved, but the system complexity increases
Solution Approach 1:
A classification model is introduced as an intermediary component between manual label assignment and final incident classification. The model receives incident descriptions and manually-assigned labels as input, processes them through machine learning algorithms, and outputs corrected classifications. This intermediary model resolves the contradiction by automating the validation process, thereby improving accuracy while managing complexity through modular design.
Solution Approach 2:
The system implements a feedback mechanism where the classification model's predictions are compared against manually-assigned labels, and mismatched cases are flagged for review. This feedback loop allows the system to continuously improve by learning from correction patterns, enhancing classification accuracy over time while maintaining a manageable complexity level through iterative refinement.
2Ease of operation
If manual classification is performed by human analysts, then the system remains simple to operate, but classification errors occur and delay threat identification
Solution Approach 1:
The classification model operates autonomously to self-correct classification errors without requiring constant human intervention. It automatically processes incident descriptions and labels, identifies mismatches, and generates corrected classifications. This self-service capability maintains ease of operation while significantly improving reliability by reducing human error in threat identification.
3Reliability
If more security alerts are generated and monitored, then better coverage of security threats is achieved, but the volume of manual classification work increases
Solution Approach 1:
The patent replaces the mechanical process of manual classification with an automated classification model based on machine learning. The model substitutes human analysts in the classification task, processing security alerts at machine speed without fatigue or error. This substitution maintains comprehensive threat coverage while dramatically increasing productivity by eliminating the bottleneck of manual classification work.
Data Source
AI summary
In accordance with the methods herein, an incident description characterizing a security incident and a manually-assigned incident characterization label characterizing the same security incident are received. The manually-assigned incident characterization label corresponds to one of a plurality of incident classification categories (including, for example, true positive, false positive) assigned by a user of a security monitoring system such as a SIEM system. A trained incident classification model is applied to the incident description, to classify the security incident in relation to the incident classification categories, thus generating a model classification prediction. The model classification prediction is compared with the manually-assigned incident characterization label and where it is determined that the manually-assigned incident characterization label and the model classification prediction are mismatched, a security mitigation action associated with the security incident is performed.