Suspicious Activity Dashboard Feedback for Alert Prioritization
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Solution Overview
Problem
Existing methods for monitoring complex software application frameworks face inefficiencies in prioritizing, deprioritizing, and identifying false positives among numerous suspicious activity alerts, especially with dynamic and interdependent services and microservices.
Innovation Solution
An interactive software application platform monitoring dashboard with alert feed and monitoring interfaces allows users to select and validate suspicious activity alerts, training predictive data analysis models for improved classification and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing predictive data analysis models are used to monitor suspicious activity alerts, then automated detection is provided, but accuracy in detecting rarer types of alerts deteriorates
Solution Approach 1:
The system implements feedback loops where user interactions with alerts (prioritization, deprioritization, false positive marking) are captured and used to continuously retrain and improve the predictive data analysis models. This allows the automated detection system to learn from actual user decisions and improve its accuracy over time, particularly for rarer alert types that provide valuable training signals.
Solution Approach 2:
The system performs preliminary classification and prioritization of alerts using the predictive models before user review, pre-processing the high volume of alerts to present only the most relevant ones to users. This preliminary action reduces the burden on users while maintaining automated detection capabilities, and the user feedback from this pre-processed set further refines the models.
2Reliability
If comprehensive monitoring of all suspicious activity alerts is performed, then detection coverage is improved, but system complexity increases
Solution Approach 1:
The system segments the monitoring function into multiple specialized components: predictive data analysis models for initial filtering, prioritization algorithms for ranking, user interface components for interaction, and feedback processing modules for model retraining. This segmentation allows comprehensive monitoring coverage while managing complexity through modular, independent components that can be developed and maintained separately.
Solution Approach 2:
The system introduces intermediary layers between raw alert data and user interaction, including predictive models that filter and score alerts, and prioritization mechanisms that rank them. These intermediaries reduce the complexity of direct user interaction with all alerts while maintaining comprehensive monitoring, by preprocessing and organizing the data before presentation to users.
3Measurement precision
If user interaction and feedback collection is implemented, then model training data quality is improved, but operational time increases
Solution Approach 1:
The system implements partial feedback collection by focusing on key user interactions (prioritization, deprioritization, false positive marking) rather than requiring comprehensive input for every alert. This selective feedback approach captures sufficient training data to improve model quality while minimizing the time users need to spend providing feedback, avoiding excessive action that would significantly increase operational time.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient monitoring of software application frameworks. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to perform software application framework monitoring using an interactive software application platform monitoring dashboard comprises a set of user interfaces (e.g., an alert feed user interface, an alert monitoring user interface, and/or the like) that enable an end user to hierarchically view event monitoring metadata fields associated with each recorded suspicious activity alert of one or more recorded suspicious activity alerts of the software application platform, provide user-selected alert validity indicators for each recorded suspicious activity alert of the recorded suspicious activity alerts, and/or generate a suspicious activity monitoring workflow for each recorded suspicious activity alert of the recorded suspicious activity alerts.