Code Fingerprinting With Statistical Vectors for Anomaly Detection
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Solution Overview
Problem
Existing cybersecurity scanning technologies are inefficient, resource-intensive, and struggle to identify new patterns or anomalies in code repositories, relying on manual pattern definition and subjective judgments, while existing solutions like static code scanners and machine learning models face limitations in reliability and computing resource usage.
Innovation Solution
A method and system for code fingerprinting that generates statistics vectors by scanning code repositories using fingerprinting code, clusters these vectors, and applies machine learning models to detect anomalies, leveraging a knowledge base for pattern identification and automatic code generation, reducing resource consumption and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static code scanners are used to scan code repositories, then security vulnerabilities can be detected, but computing resources are excessively consumed and scanning efficiency is low
Solution Approach 1:
The patent segments the code repository into multiple software components and processes them in parallel using a distributed scanning architecture. The code repository is divided into chunks that can be independently scanned by multiple worker nodes, reducing the computational burden on single systems and enabling efficient resource utilization across distributed infrastructure.
Solution Approach 2:
The patent transforms code into statistical vectors by changing the representation parameters from raw code text to numerical statistics. This parameter transformation enables the use of efficient vector similarity search and clustering algorithms instead of traditional resource-intensive static analysis, maintaining detection capability while reducing computing resource consumption.
2Reliability
If traditional code scanning methods are used, then security analysis can be performed, but scanning time is excessive and productivity is low
Solution Approach 1:
The patent performs preliminary transformation of code into statistical vectors before scanning. By pre-processing code into compact vector representations that capture essential security characteristics, the system enables rapid similarity comparison and anomaly detection without requiring time-consuming traditional static analysis during the scanning phase.
Solution Approach 2:
The patent creates simplified copies of code in the form of statistical vectors that retain security-relevant information while discarding unnecessary details. These vector copies enable fast comparison and clustering operations, dramatically increasing scanning speed while maintaining the ability to detect security issues.
3Measurement precision
If manual pattern definition is used in existing scanning solutions, then specific vulnerabilities can be detected, but the system cannot identify new patterns and requires subjective judgments
Solution Approach 1:
The patent enables the scanning system to automatically learn and adapt to new vulnerability patterns without manual intervention. By using unsupervised learning algorithms on statistical vectors, the system self-organizes into clusters that represent different software component types and automatically identifies anomalies, eliminating the need for manual pattern definition while maintaining detection accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where detected anomalies and cluster structures inform continuous improvement of the scanning model. The system learns from detected patterns and adjusts its clustering and anomaly detection parameters, enabling automatic adaptation to new vulnerability types while maintaining precision through iterative refinement.
Data Source
AI summary
A system and method for code fingerprinting. A method includes scanning a code repository including software components by executing fingerprinting code on the code repository in order to generate statistics vectors for the software components. The fingerprinting code includes instructions for performing a text search to identify instances of patterns in the code repository and to generate the statistics vectors based on the identified pattern instances. The statistics vectors are clustered with respect to groupings of software components. The statistics vectors are aggregated by combining values of respective statistics in the statistics vectors belonging to the same clusters. An anomaly is detected based on the aggregated statistics vectors. One or more remedial actions are performed with respect to the software components based on the detected anomaly.