Adaptive Machine Learning for Dynamic File Control
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Solution Overview
Problem
Static management systems in computer networks are inadequate in adapting to changing file control requirements, leading to improper rejection of legitimate files due to inflexible filtering mechanisms.
Innovation Solution
An adaptive machine learning system that learns from past data to dynamically adjust file controls, using a processor and memory to determine file properties and adjust thresholds, ensuring accurate filtering of files without rejecting legitimate ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static file control systems are used to filter incoming files, then file security control is maintained, but legitimate files are improperly rejected due to inflexible filtering mechanisms
Solution Approach 1:
The patent implements dynamic file control by using machine learning models that continuously learn from file patterns and automatically adjust control parameters. The system transitions from static, pre-defined file controls to dynamic, adaptive controls that evolve with changing file characteristics and security requirements, preventing false rejections while maintaining security.
Solution Approach 2:
The system changes control parameters based on learned patterns from training data. The machine learning model adjusts file control parameters dynamically by analyzing historical file data and identifying patterns, allowing the system to adapt control thresholds and criteria without manual intervention, thereby improving accuracy while maintaining adaptability.
2Device complexity
If static filtering mechanisms are used to manage computer networks, then system simplicity is maintained, but the systems become increasingly unworkable as networks grow more complex
Solution Approach 1:
The patent implements self-service network management through machine learning models that automatically analyze file patterns, learn from historical data, and adjust control parameters without human intervention. The system serves itself by autonomously adapting to network changes, eliminating the need for manual reconfiguration and maintaining effectiveness as networks grow more complex.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously receives information about file characteristics and control outcomes, then uses this feedback to refine its predictions and adjust parameters. This closed-loop feedback enables the system to automatically improve its performance and adapt to changing network conditions without increasing operational complexity.
Data Source
AI summary
An adaptive machine learning system for predicting file controls includes a memory, an interface, and a processor. The memory stores a plurality of controls for incoming files and the interface receives a first file and a second file. The first file has a first property and the second file has a second property. The processor determines a type for each of the first property and the second property, wherein the type of each property is related to a first file control. The processor also determines that the first property and the second property each satisfy the first file control. If the value of the first property and the second property are above a first threshold, the processor changes a value of the first control for incoming files.