AI Hash Pattern Detection of Hidden Executable Media Files
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Solution Overview
Problem
Hidden executable files pose security threats by disrupting computers, servers, or networks, leaking information, gaining unauthorized access, or compromising user privacy, and existing detection methods are inadequate.
Innovation Solution
A system utilizing artificial intelligence (AI) and hashing algorithms to generate and compare hash values, followed by AI-based image analysis, to identify and respond to hidden executable files in various media types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used to identify hidden executable files, then the detection process is simple, but the detection accuracy is insufficient and cannot reliably distinguish hidden executables from legitimate media files
Solution Approach 1:
The detection process is divided into multiple stages: first generating hash values from media files, then comparing these hash values against a database of known executable file hashes, and finally using AI algorithms to analyze patterns. This segmentation allows each component to specialize in one aspect of detection, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
Hash values serve as an intermediary representation between the original media files and the detection system. By converting files into hash values, the system can efficiently compare and analyze file contents without processing the entire media file, thereby improving detection accuracy while reducing computational complexity.
2Speed
If hash value comparison is performed for every received file, then detection speed is fast, but false positives occur when legitimate files have similar hash patterns
Solution Approach 1:
The system uses AI algorithms to analyze patterns in hash values and learn from previous detection results. This feedback mechanism allows the system to distinguish between legitimate files with similar hash patterns and actual hidden executables, reducing false positives while maintaining fast detection speed through pattern recognition rather than exhaustive comparison.
Solution Approach 2:
The system transforms the detection problem from direct file comparison to hash value pattern analysis. By changing the parameter being analyzed from raw file data to derived hash patterns, the system achieves both fast processing speed and high reliability through statistical pattern recognition and AI-based classification.
3Measurement precision
If AI-based analysis is applied to all files, then detection accuracy improves, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary hash value generation and comparison before applying computationally intensive AI analysis. This preliminary action filters out obviously safe files using fast hash comparison, reserving AI-based analysis only for files that require deeper inspection. This approach maintains high detection accuracy while significantly reducing overall processing time by avoiding unnecessary AI computation on benign files.
Data Source
AI summary
A system for detecting hidden executable files accesses a first image, generates a first hash value for the first image based at least in part upon a hashing algorithm, determines a first pattern of the first hash value and multiple patterns of stored hash values corresponding to multiple images without hidden executable files, determines whether the first pattern matches any of the patterns based on a comparison between the first pattern and each of the patterns, and determines the first image includes a hidden executable file if the first pattern does not match any of the patterns.