Coordinate-Based Data Protection for Adaptive Malware Detection
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Solution Overview
Problem
Anti-malware tools are slow to adapt to changing malware, require exact matches, and rely heavily on human intervention, leaving computing devices vulnerable for extended periods.
Innovation Solution
Representing data in n-dimensional coordinates and using machine-trained models, such as artificial neural networks, to efficiently and accurately detect threats like malware by processing data at a bit or byte level, enabling rapid adaptation and reducing human involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-malware tools use pattern matching and hash matching to identify malware, then they can detect known malware, but they require exact matches and are slow to adapt to changing malware
Solution Approach 1:
The patent transforms malware detection from exact pattern matching to coordinate-based geometric analysis. By converting binary data into n-dimensional coordinate representations and analyzing geometric relationships (distances, angles, clusters), the system can detect malware based on structural similarities rather than exact matches, enabling adaptation to mutated malware while maintaining detection accuracy
Solution Approach 2:
The patent replaces traditional mechanical pattern-matching algorithms with machine learning models trained on coordinate representations. This substitution enables the system to learn complex patterns and relationships in malware data, improving both detection accuracy and adaptability to new malware variants without requiring manual rule updates
2Reliability
If anti-malware tools rely on human intervention to flag or verify malware, then they can verify threats, but they are slow to process data and leave devices vulnerable for extended periods
Solution Approach 1:
The patent implements self-service through automated machine learning models that perform both detection and verification without human intervention. The system processes data in real-time, automatically flagging and verifying threats through coordinate analysis and geometric pattern recognition, eliminating the bottleneck of manual verification while maintaining high accuracy through trained models
Solution Approach 2:
The patent performs preliminary action by continuously analyzing data in real-time before threats can cause significant damage. The coordinate-based representation and machine learning models enable proactive detection of malware patterns as they appear, allowing the system to respond to threats immediately rather than waiting for human verification
3Reliability
If anti-malware tools use traditional pattern matching techniques, then they can identify malware, but they are slow to process data and require exact matches
Solution Approach 1:
The patent applies dimensionality change by converting one-dimensional binary data into n-dimensional coordinate representations. This transformation enables parallel processing of multiple data features simultaneously through geometric operations, dramatically increasing processing speed while maintaining detection accuracy through comprehensive analysis of data relationships across multiple dimensions
Data Source
AI summary
Techniques and architectures for representing data with one or more n-dimensional representations and/or using one or more models to identify malware are described herein. For example, the techniques and architectures may determine one or more coordinates for one or more points based on one or more sets of bits in the data and generate an n-dimensional representation for the data based on the one or more points. The techniques and architectures may evaluate the n-dimensional representation with one or more machine-trained models to detect malware.