Alert Threshold Classification Using ML to Cut False Positives
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Solution Overview
Problem
Existing dataset classification systems face challenges in optimizing thresholds and scores for accurate classification, leading to high false positive alerts, which can overwhelm investigators and result in improper actions.
Innovation Solution
A machine learning model, such as a decision tree algorithm, is used to assess relationships between datasets and dynamically adjust thresholds, reducing false positive categorizations by generating new threshold sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rule-based classifications are used to categorize datasets, then the classification process is simple and fast, but the number of false positive alerts increases significantly
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the rule-based classification system and the final alert generation. The ML model receives inputs from rule-based classifications and outputs refined categorizations, acting as a mediator that filters false positives while maintaining processing efficiency. This intermediary layer enables the system to combine the speed of rule-based processing with the accuracy of machine learning.
Solution Approach 2:
The system dynamically adjusts classification parameters by generating multiple threshold sets through machine learning simulations. Instead of using fixed thresholds, the system optimizes parameters such as threshold values and score adjustments based on simulated impacts, allowing adaptive parameter changes that reduce false positives while maintaining high classification speed.
2Measurement precision
If threshold and score parameters are optimized manually to improve classification accuracy, then the classification precision improves, but the complexity and time required for optimization increases significantly
Solution Approach 1:
The system performs self-optimization by automatically generating multiple threshold sets and simulating their impacts without requiring manual intervention. The machine learning model autonomously evaluates different parameter combinations and selects optimal thresholds, enabling the system to improve its own classification accuracy without external optimization efforts, thus reducing operational complexity.
Solution Approach 2:
The system performs preliminary simulations and pre-calculations to determine optimal threshold sets before actual classification operations. By conducting impact simulations in advance and pre-optimizing parameters, the system avoids complex real-time optimization during classification, reducing operational complexity while maintaining high accuracy.
3Measurement precision
If more threshold parameters are changed to improve classification accuracy, then the classification precision improves, but the manageability and operational feasibility decreases
Solution Approach 1:
The manual mechanical process of adjusting and managing multiple threshold parameters is replaced with an automated machine learning system. Instead of manually manipulating numerous parameters, operators interact with a simplified interface that presents optimized threshold sets generated by the ML model, substituting complex manual operations with automated intelligence while maintaining ease of operation.
Solution Approach 2:
The system implements feedback mechanisms where classification results and their impacts are continuously fed back into the machine learning model to refine future threshold recommendations. This feedback loop enables the system to learn from past performance and automatically adjust parameters, reducing the manual management burden while improving accuracy over time.
Data Source
AI summary
A system and method for identifying data connections may submit alert data items of one or more datasets to a machine learning model, wherein the alert data items of each dataset include: an alert rule that initiated an alert for the dataset including a first set of thresholds, one or more data values assessed by the first set of thresholds in the generation of the alert, and an alert categorization of the alert selected from a true positive categorization or a false positive categorization; assess, combinations of the alert categorization in relation to the one or more data values and the alert rule; generate a second set of thresholds for the one or more data values, wherein the second set of thresholds has a reduced false positive categorization of the alerts compared to the first set of thresholds; and update the alert rule to comprise the second set of thresholds.