ML Ensemble Anomaly Detection for Hardware Firmware Software
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Solution Overview
Problem
Existing methods for generating and handling problem tickets for machine anomalies are manually intensive, error-prone, and non-uniform, relying on human expertise, which can lead to inconsistent and inefficient anomaly resolution.
Innovation Solution
A computer-implemented method using machine learning to detect anomalies in hardware, firmware, and software events, determining if they are duplicates, and addressing them based on previous similar anomalies, employing an ensemble model combining rule-based, deep learning, and random forest classifiers to automate the anomaly handling process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to generate and handle problem tickets for machine anomalies, then human expertise can be applied to determine appropriate fixes, but the process becomes manually intensive, error-prone, and non-uniform
Solution Approach 1:
The system enables self-service anomaly handling by automatically detecting anomalies, determining their status through machine learning models, and resolving duplicates without human intervention. The ensemble model autonomously classifies anomalies and applies appropriate actions, eliminating the need for manual review while maintaining consistent decision-making across all anomaly cases.
Solution Approach 2:
The patent replaces the manual mechanical process of human review and decision-making with an automated machine learning system. The ensemble model consisting of rule-based, deep learning, and random forest classifiers substitutes human experts, providing consistent, error-free, and uniform anomaly resolution across all cases without human variability.
2Productivity
If manual review of each problem ticket is performed by developers, then accurate determination of anomalies can be made, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical anomaly data and establishing decision rules in advance. When new anomalies occur, the pre-trained ensemble model immediately classifies and resolves them without requiring real-time human analysis, dramatically reducing handling time while maintaining accuracy through previously learned patterns.
Solution Approach 2:
The automated system handles anomaly resolution independently without requiring developer time for review. The machine learning model autonomously determines anomaly status, identifies duplicates, and applies appropriate fixes, freeing developers from manual ticket review and significantly increasing overall productivity in anomaly management.
3Reliability
If human expertise is relied upon for anomaly determination, then nuanced judgment can be applied, but the process becomes non-uniform and error-prone
Solution Approach 1:
The patent employs a composite approach by combining three different machine learning models (rule-based, deep learning, and random forest classifiers) into an ensemble system. Each model contributes its strengths to the overall anomaly detection process, creating a more robust and reliable system that leverages multiple approaches rather than relying on a single method, thereby improving accuracy while managing complexity through structured integration.
Data Source
AI summary
Aspects of the invention include detecting an anomaly in a database of hardware, firmware, and software events. An exemplary method includes determining whether a previously addressed anomaly is a duplicate of the anomaly, addressing the anomaly according to a state of the previously addressed anomaly based on the previously addressed anomaly being a duplicate of the anomaly, and addressing the anomaly according to machine learning based on the previously addressed anomaly not being the duplicate of the anomaly.