Knowledge-Based Code Fingerprinting for Statistical Anomaly Detection
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Solution Overview
Problem
Existing cybersecurity scanning technologies are inefficient, resource-intensive, and unable to effectively identify new patterns or anomalies in code repositories, relying on manual pattern definition and subjective judgments, and existing solutions like static code scanners and large language models are slow and costly.
Innovation Solution
A method and system for code fingerprinting using a knowledge base to generate fingerprinting code that performs a text search and generates statistical data, allowing for efficient scanning and anomaly detection through statistical analysis and machine learning models, reducing computing resources and improving scanning speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static code scanners and large language models are used for code scanning, then comprehensive code analysis can be performed, but scanning speed is slow and computing resources are excessive
Solution Approach 1:
The patent extracts only the essential pattern-matching functionality from complex code analysis tools. Instead of using entire large language models or comprehensive static code scanners, the invention extracts specific fingerprinting patterns and rules that capture the most critical security concerns, performing only the necessary text search and statistical analysis needed for effective scanning.
Solution Approach 2:
The codebase is segmented into distinct fingerprinting patterns organized in a knowledge base. Each pattern represents a specific security concern or code characteristic that can be independently identified and analyzed. This segmentation allows the scanning system to process code in targeted, efficient passes rather than attempting comprehensive analysis all at once.
2Adaptability or versatility
If manual pattern definition is used in existing scanning technologies, then scanning can be performed, but the process is resource-intensive and unable to effectively identify new patterns or anomalies
Solution Approach 1:
The system performs self-service by automatically generating new fingerprinting patterns through statistical analysis of the codebase. Instead of requiring manual updates to pattern definitions, the scanning tool autonomously identifies new patterns and anomalies by analyzing statistical deviations in the code, continuously adapting without additional human intervention or excessive computing resources.
Solution Approach 2:
The patent implements feedback mechanisms where scanning results and statistical data are continuously analyzed to refine and update fingerprinting patterns. The system uses the output of each scanning operation to improve future scanning accuracy, allowing patterns to evolve and adapt to new code structures and security threats automatically.
3Reliability
If frequent and prompt scanning is performed to catch cyber threats early, then security detection capability is improved, but computing resource consumption increases
Solution Approach 1:
The patent applies partial action by performing targeted text searches for specific fingerprinting patterns rather than conducting exhaustive code analysis. The system focuses computational effort only on identifying patterns that match known security concerns and statistical anomalies, performing just enough analysis to detect threats without the overhead of comprehensive code review.
Data Source
AI summary
A system and method for code fingerprinting. A method includes generating fingerprinting code based on a knowledge base, wherein the knowledge base includes a plurality of nodes representing respective software components of a plurality of software components, wherein the fingerprinting code includes instructions that, when executed by a processing circuitry, configure the processing circuitry to perform a text search in order to identify patterns in at least one code repository defined with respect to the knowledge base and to generate statistical data about the identified patterns; and causing the fingerprinting code to run on the at least one code repository, wherein causing the fingerprinting code to run further comprises executing the instructions of the fingerprinting code in order to scan the at least one code repository.