Continual Threat Detection Models for Zero-Trust Architectures
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Solution Overview
Problem
Existing zero-trust systems face challenges in detecting and responding to both known and unknown threats due to the resource-intensive nature of training separate models for each threat, leading to storage constraints, manageability issues, and catastrophic forgetting.
Innovation Solution
A continual learning approach for threat detection using a single machine learning model that adapts over time to detect new threats without forgetting existing ones, utilizing class-incremental learning to update the model with new data while maintaining accuracy on previous threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a separate machine learning model is created for each threat, then the ability to detect specific threats is improved, but storage requirements and system complexity increase significantly
Solution Approach 1:
The patent merges multiple threat detection models into a single unified model that can detect multiple threat types. This consolidation reduces the number of separate models from many to one, directly addressing the complexity and storage issues while maintaining the ability to detect various threats through a shared learning architecture.
Solution Approach 2:
The unified threat detection model is designed with multi-functionality to detect different threat types using a single model structure. The model learns to identify various threat patterns through a common framework, eliminating the need for separate specialized models for each threat type while preserving detection accuracy.
2Reliability
If multiple machine learning models are trained for different threats, then detection accuracy for specific threats improves, but resource usage and storage requirements increase
Solution Approach 1:
The patent combines multiple threat detection functions into a single model, reducing the total storage requirements from storing multiple complete models to storing one shared model. This merging approach maintains detection accuracy by preserving the essential learning capabilities across different threat types within the unified architecture.
Solution Approach 2:
The universal threat detection model performs multiple detection functions simultaneously, eliminating the need to store separate models for each threat type. This multi-functional design reduces the quantity of data that must be stored while maintaining the accuracy needed for detecting various threat patterns.
3Adaptability or versatility
If models are updated to detect new threats, then detection of new threats is improved, but catastrophic forgetting removes the ability to detect known threats
Solution Approach 1:
The patent implements continual learning where the unified model continuously learns from new threat data without forgetting previous threat detection capabilities. The model maintains continuous adaptation to new threats while preserving knowledge of known threats through ongoing training that reinforces existing patterns rather than replacing them.
Solution Approach 2:
The system uses feedback mechanisms where detection results and threat data continuously inform model updates. This feedback loop ensures that learning new threats does not erase knowledge of known threats, as the model refines its patterns through continuous validation against both new and existing threat data.
Data Source
AI summary
Threat detection in computing architectures is disclosed. A model is trained to classify files. The files may each be classified as safe or as a threat. Files that are not classified as safe with a sufficient confidence level and may represent a new threat are forwarded for further analysis at a central server and/or with expert input. If the new threat is verified, the model is adapted such that the model can recognize the new threat without forgetting the threats previously learned. The adapted or updated model is deployed to the endpoint and the current version of the model is replaced with the updated model.