AI Scannable Code Detection Using Metadata Confidence Scoring
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Solution Overview
Problem
There is a need for a secure and reliable method to detect potentially unauthorized computer code, particularly in mobile devices, where the differences between legitimate and unauthorized scannable codes are difficult to distinguish, posing security risks.
Innovation Solution
A system using an artificial intelligence-based analyzer maintains a repository of scanned codes with metadata, employing convolutional neural networks and search algorithms to analyze and compare scanned codes, determining their safety through confidence scoring and presenting notifications or blocking execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional code comparison methods are used, then the system is simple to implement, but the accuracy of detecting unauthorized code is insufficient
Solution Approach 1:
The patent replaces traditional mechanical code comparison methods with an AI-based analyzer that uses machine learning algorithms to detect unauthorized code. The AI model processes code patterns, metadata, and behavioral characteristics to identify malicious code with high accuracy, substituting simple string matching or signature-based detection with intelligent analysis that adapts to new threats.
Solution Approach 2:
The system changes the parameters of code analysis by incorporating multiple dimensions including code structure, metadata attributes, execution patterns, and historical data. Instead of relying on single-parameter matching, the AI analyzer evaluates multiple parameters simultaneously to determine code safety, enabling more precise detection while managing complexity through structured parameter evaluation.
2Measurement precision
If AI-based analysis is implemented, then the detection accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing code data into structured formats, pre-training AI models on extensive code repositories, and pre-establishing decision thresholds. This preliminary preparation enables the AI analyzer to make rapid predictions during actual code scanning, reducing real-time processing delays while maintaining high detection accuracy through pre-computed features and optimized model inference.
Solution Approach 2:
The AI-based analysis process is segmented into multiple independent stages including code preprocessing, feature extraction, pattern recognition, and final classification. Each segment can be processed independently and in parallel, allowing the system to distribute computational load and reduce overall processing time while maintaining comprehensive analysis for accurate detection.
3Reliability
If comprehensive metadata analysis is performed, then the reliability of detection increases, but the computational resources required increase
Solution Approach 1:
The system extracts only the most relevant metadata features and code characteristics needed for detection, rather than analyzing all available data. The AI analyzer identifies and focuses on critical parameters such as code structure patterns, suspicious metadata fields, and high-risk behavioral indicators, discarding redundant information to reduce computational energy consumption while maintaining detection reliability.
Solution Approach 2:
The system applies partial analysis by performing comprehensive metadata examination only when initial screening identifies suspicious patterns. For clearly benign or obviously malicious code, the system uses streamlined analysis with fewer computational resources, allocating full processing power selectively to cases where comprehensive analysis is necessary to maintain reliability without wasteful energy consumption on all inputs.
Data Source
AI summary
A system is provided for detection of unauthorized computer code using an artificial intelligence-based analyzer. The system may include an artificial intelligence-based analyzer configured to receive various data and metadata from a user computing device when the user computing device is used to capture a scannable code using an image capture device. The system may, using the AI-based engine, compare the scanned code with a pool or repository of previously scanned code to determine whether the code has been scanned before. The repository may further comprise various types of metadata regarding the scanned code, such as success rate, usual scanning location, embedded resources or links within the code, timestamps for when the code was previously scanned, and/or the like. Based on the analysis of the scanned code, the system may present a notification on the user computing device that indicates whether the scanned code is potentially unsafe to execute.