Malware Detection via Revision Control Log Analysis
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Solution Overview
Problem
Large organizations face difficulties in detecting and neutralizing logic bombs and other malware embedded in their software by developers, as these malicious codes often resemble legitimate code and can be hidden amidst vast amounts of legitimate code in source code repositories.
Innovation Solution
A system that analyzes revision control logs to identify potentially malicious code changes by evaluating risk factors, flags suspicious changes, and subjects them to manual review, thereby reducing resource consumption and improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all code changes are manually reviewed to detect malware, then detection reliability is improved, but productivity deteriorates due to the vast amount of code
Solution Approach 1:
The patent segments the code review process into two distinct phases: automated analysis phase using risk factors to identify suspicious changes, and manual review phase for only those flagged changes. This segmentation resolves the contradiction by automating the initial filtering step, maintaining high detection reliability while dramatically improving productivity by reducing the volume of code requiring manual inspection.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between code submission and manual review. This intermediary evaluates code changes against multiple risk factors (e.g., unusual patterns, developer behavior, code characteristics) and selectively flags suspicious changes for manual review, thereby maintaining detection reliability while improving overall review productivity.
2Productivity
If automated analysis is used to screen code changes, then productivity is improved, but measurement precision deteriorates due to potential false positives
Solution Approach 1:
The patent employs multiple risk factors as adjustable parameters to characterize code changes (e.g., code complexity, developer history, change patterns). By evaluating changes against this multi-parameter risk profile, the system achieves both high productivity through automated screening and maintains precision by using sophisticated parameter-based differentiation to minimize false positives.
Solution Approach 2:
The patent implements feedback mechanisms where manual review outcomes are used to refine and adjust the automated analysis parameters. This feedback loop continuously improves the precision of automated detection while maintaining high productivity, as the system learns from actual cases to better distinguish malicious from legitimate code changes.
3Measurement precision
If comprehensive risk factors are evaluated for each code change, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the comprehensive risk factor evaluation into modular, independent assessment components. Each risk factor (e.g., code patterns, developer behavior, temporal analysis) is evaluated separately and then aggregated, which maintains high detection precision while managing system complexity through modular design that allows independent development and tuning of each risk factor.
Data Source
AI summary
Methods, systems, computer-readable media, and apparatuses for detecting malware using revision control logs are presented. In some embodiments, a computing device may gather one or more revision control logs, and the one or more revision control logs may identify one or more code changes. Subsequently, the computing device may determine, based on one or more risk factors, that at least one code change identified in the one or more revision control logs is potentially malicious. Based on determining that the at least one code change is potentially malicious, the computing device may generate a notification indicating that the at least one code change is potentially malicious.