Continual Malware Detection in Zero-Trust Architectures for Unknown Threats
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Solution Overview
Problem
Existing malware detection models struggle to adapt to evolving threats and require significant human effort for data annotation, leading to inefficiencies and increased processing and storage costs.
Innovation Solution
A continual learning framework that uses a Gaussian mixture model and label propagation to automatically detect new malware threats in a zero-trust environment, adapting the model without forgetting existing threats and reducing the need for new model creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional malware detection models are used, then known malware can be detected, but the models cannot adapt to evolving malware threats
Solution Approach 1:
The patent implements a dynamic model updating mechanism where the malware detection model is continuously retrained with new malware samples. The system automatically detects new malware, adds samples to the training set, and retrains the model without manual intervention, making the detection capability evolve dynamically alongside malware threats while maintaining reliability through systematic updates
Solution Approach 2:
The system performs self-updating by automatically detecting new malware threats, selecting representative samples, adding them to the training dataset, and retraining the model autonomously. This self-service mechanism eliminates the need for manual model updates while maintaining continuous adaptability to new threats
2Measurement precision
If specialists manually analyze and annotate malware data, then detection models can be trained, but it requires substantial time and effort
Solution Approach 1:
The system automatically performs data annotation by detecting new malware threats and selecting representative samples for the training set without requiring specialist intervention. The automated selection process identifies meaningful samples based on detection confidence and diversity criteria, eliminating time-consuming manual annotation while maintaining data quality
Solution Approach 2:
The patent introduces an automated sample selection mechanism as an intermediary between malware detection and model training. This intermediary automatically identifies and selects representative malware samples from detected threats, serving as a bridge that replaces manual specialist analysis with algorithmic selection while preserving annotation quality
3Adaptability or versatility
If new malware threats are detected, then security improves, but creating a new model increases processing and storage cost
Solution Approach 1:
The patent merges new malware detection capabilities with the existing detection model by continuously updating the same model with new samples. Instead of creating separate models for different threat types, the system combines all malware detection knowledge into a single evolving model, reducing processing and storage overhead while maintaining comprehensive threat detection
Solution Approach 2:
The system implements continuous model updating where the detection model is constantly refined with new malware samples rather than being recreated periodically. This continuous action maintains current detection capabilities while incrementally improving the model, avoiding the computational overhead of creating entirely new models and reducing storage requirements for multiple model versions
Data Source
AI summary
One example method includes deploying a malware detection model in a production environment, performing a monitoring process that comprises capturing data from the production environment, by the malware detection model, determining, by the malware detection model, that a likelihood that the data belongs to a domain known to the malware detection model falls below a threshold, determining, by the malware detection model, whether or not the data is noise, or comes from a new domain not known to the malware detection model, and when it is determined that the data comes from the new domain, adapting the malware detection model by incorporating knowledge about the new domain in the malware detection model so that the malware detection model is operable to detect malware in the new domain, as well as the known domain.