ML Control Fault Detection via Vector Space Mapping
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Solution Overview
Problem
Existing systems fail to effectively identify and modify faulty controls in computing systems, making it difficult to prevent incidents such as cybersecurity incidents, as they struggle to determine which controls are related to an incident and require extensive resources for training machine learning models.
Innovation Solution
The use of machine learning to map incident data and control data to the same vector space, identifying problematic code and generating recommendations for modifications, along with a weighting mechanism to improve the quality and efficiency of training data for machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained to identify faulty controls, then the ability to detect and prevent incidents is improved, but the training resources and time required increase significantly
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data from historical incident data and control code before actual ML model training is needed. This pre-processing of training data reduces the time and resources required for model training while maintaining high incident detection capability
Solution Approach 2:
The patent introduces an intermediary component that synthesizes training data from historical incidents and control code. This intermediary layer transforms raw historical data into structured training samples, reducing the burden of manual data collection and preparation while improving training efficiency
2Reliability
If machine learning models are trained to identify faulty controls, then the ability to detect and prevent incidents is improved, but the computing resources required for training increase
Solution Approach 1:
The training data generation process is segmented into distinct components: historical incident data extraction, control code analysis, synthetic sample generation, and validation. This segmentation allows for optimized resource allocation at each stage and enables parallel processing of different data generation tasks, reducing overall computing resource requirements
Solution Approach 2:
The system creates synthetic copies of training data by synthesizing new training samples from historical incident data and control code patterns. These synthetic copies serve as effective training data without requiring additional real-world incident data collection, reducing the computational burden of data gathering while maintaining training quality
3Measurement precision
If existing systems attempt to identify which controls are related to incidents, then incident analysis capability is improved, but the complexity of analyzing thousands of controls makes it difficult to determine which controls should be modified
Solution Approach 1:
The system extracts and isolates the specific portion of control code that is related to an incident by analyzing the relationship between incident data and control code. This extraction process identifies only the relevant segments of controls that need modification, reducing the complexity of analyzing thousands of controls by focusing only on the problematic portions
4Manufacturing precision
If manual analysis is used to determine which controls should be modified, then control modification accuracy is improved, but the time and resources required increase significantly
Solution Approach 1:
The system performs self-service by automatically generating synthetic training data, training the ML model, and identifying control modifications without requiring extensive manual analysis. The automated system maintains high modification accuracy by using historically accurate incident-data relationships while significantly improving productivity through automation
Data Source
AI summary
In some aspects, a computing system may use machine learning to determine whether a control is faulty or generate recommendations to make a modification to a control. may identify a portion of problematic computer code that implements the faulty control through the use of machine learning. A computing system may use machine learning to generate embeddings that map incident data and control data (e.g., computer-readable code of a control) to the same vector space. Further, a computing system may use a weighting mechanism that may be used to weight each sample used to train a machine learning model, which may allow a model to train more efficiently.