Malware DNA Identification via NLP and Machine Learning
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Solution Overview
Problem
Existing malware detection systems rely on log analysis and statistical numerical analysis, which are inadequate in identifying the evolving nature of malware, as they do not effectively utilize machine learning and natural language processing to detect malware DNA.
Innovation Solution
A system and method utilizing machine learning and natural language processing to identify malware DNA by analyzing text strings in sample malware, comparing them to prototype malwares, and generating a classification to determine similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If log analysis and statistical numerical analysis are used for malware detection, then the detection process is simple, but the ability to identify evolving malware is inadequate
Solution Approach 1:
The patent replaces traditional log analysis and statistical numerical analysis (mechanical/systematic approaches) with machine learning models and natural language processing. The system extracts text strings from malware code and applies NLP techniques to analyze linguistic patterns, while machine learning models classify malware based on these text features, thereby improving detection accuracy for evolving malware without significantly increasing operational complexity
Solution Approach 2:
The patent changes the analysis parameters from numerical statistics to text-based linguistic features. By extracting and analyzing text strings from malware code using NLP, the system transforms the detection approach to focus on linguistic patterns and semantic meanings, enabling better identification of malware DNA and evolution patterns while maintaining system manageability
2Measurement precision
If machine learning and natural language processing are applied to analyze text strings, then malware DNA identification is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the malware analysis process into distinct modules: text string extraction, natural language processing, machine learning classification, and result interpretation. This modular segmentation allows each component to specialize in one aspect of the analysis, improving overall precision while making the complex system more manageable and maintainable through clear separation of concerns
3Adaptability or versatility
If traditional code analysis and behavioral analysis are used, then the analysis method is straightforward, but the detection of similar malware patterns is limited
Solution Approach 1:
The patent creates a universal analysis framework that handles multiple malware types and patterns through a single integrated system. The machine learning models are trained on diverse malware datasets and can generalize to identify patterns across different malware families, while the NLP component provides a unified approach to text string analysis that works across various malware types, thereby improving adaptability without requiring separate specialized systems for each malware category
Data Source
AI summary
A system and method utilize machine learning and natural language processing to identify malware DNA of a sample malware. The sample malware is analyzed for text strings using natural language processing, and machine learning models are applied to the text strings to classify the text strings as malware DNA relative to prototype malwares.