Machine Learning Threat Discernment Model Version Mismatch Handling
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Solution Overview
Problem
Traditional computer security detection methods relying on signatures, filters, and human-generated classifications struggle to keep pace with rapidly changing threats, particularly advanced threats that use 'exploits and evasion' tactics, leading to potential disruptions and inefficiencies due to mismatches in threat assessments between different versions of machine learning models.
Innovation Solution
Implementing a cloud-based, non-signature, non-heuristic machine learning threat discernment system that uses advanced mathematical analysis and ensemble models to provide real-time threat assessments, allowing for dynamic feature collection and enforcement, with mechanisms to handle mismatches between model versions by prioritizing human-generated classifications and enabling flexible user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional signature-based detection methods are used, then implementation simplicity is maintained, but detection effectiveness against advanced threats deteriorates
Solution Approach 1:
The patent replaces traditional signature-based mechanical detection methods with machine learning-based probabilistic models. The system uses supervised learning algorithms that analyze dynamic features and behavioral patterns of files, substituting static signature matching with adaptive mathematical models that can identify advanced threats through probabilistic classification.
2Measurement precision
If machine learning model versions are updated frequently to improve threat detection, then detection accuracy improves, but model mismatch and classification inconsistency worsen
Solution Approach 1:
The patent implements preliminary actions by maintaining multiple versions of machine learning models and pre-establishing mismatch detection mechanisms. Before deploying a new model version, the system detects mismatches between different model versions and implements corrective actions to ensure consistent classification, preventing instability before it occurs.
Solution Approach 2:
The system continuously monitors classification results across different model versions and implements feedback loops. When mismatches are detected between model versions, the system analyzes the discrepancies and adjusts classifications to maintain consistency, ensuring that detection accuracy improvements do not compromise model stability.
3Reliability
If real-time threat analysis is performed on all files, then security coverage is improved, but system performance and user operation efficiency deteriorate
Solution Approach 1:
The patent applies partial action by performing comprehensive machine learning-based threat analysis only on files that exhibit suspicious characteristics or fail initial filtering stages. Rather than analyzing every file in real-time, the system selectively applies intensive analysis to high-risk candidates, maintaining security coverage while preserving user operation efficiency for normal files.
Data Source
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AI summary
A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.