Code Commit Anomaly Detection for Faster Risk Review
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Solution Overview
Problem
Existing methods for detecting anomalies in code commits are time-consuming and prone to high false positives, failing to effectively identify potential vulnerabilities or backdoors introduced by malicious insiders.
Innovation Solution
Anomaly detection techniques that analyze behaviors of code committers and expected behaviors of code commits, using attribute values and machine learning models to generate anomaly reports, focusing on identifying unusual patterns and potential backdoors in source code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods review source code for potential risks in a blanket way, then comprehensive risk coverage is achieved, but time consumption increases and false positives increase
Solution Approach 1:
The patent segments the source code review process into two distinct phases: (1) behavioral analysis of code commits using machine learning to identify anomalous patterns, and (2) focused risk review only of commits flagged as anomalous. This segmentation eliminates the need for blanket review of all code, significantly reducing time consumption while maintaining detection accuracy by concentrating resources on high-risk areas.
Solution Approach 2:
Instead of performing complete risk review on all code commits (excessive action), the patent applies partial review only to commits identified as anomalous through behavioral analysis. This partial action approach reduces false positives by avoiding unnecessary review of normal commits while maintaining comprehensive coverage of potentially malicious changes.
2Reliability
If existing methods review source code for potential risks in a blanket way, then comprehensive risk coverage is achieved, but false positives increase
Solution Approach 1:
The patent performs preliminary behavioral analysis of code commits using machine learning models before conducting detailed risk review. This preliminary action identifies anomalous patterns such as unusual commit timing, atypical code changes, or suspicious committer behavior, allowing the system to pre-filter commits that warrant full risk review and eliminate those that are normal, thereby reducing false positives.
Solution Approach 2:
The system implements feedback loops where anomaly detection results from behavioral analysis feed into the risk review process, and outcomes from risk review feed back to refine the machine learning models. This feedback mechanism continuously improves detection accuracy and reduces false positives by learning from actual anomaly patterns observed in the codebase.
3Reliability
If comprehensive source code review is performed, then all potential risks are identified, but detection efficiency decreases
Solution Approach 1:
The patent introduces dynamic prioritization where the depth and scope of code review adapt based on anomaly scores generated by behavioral analysis. Commits with high anomaly scores receive comprehensive review, while low-score commits receive minimal or no review. This dynamic approach optimizes detection efficiency by allocating review resources proportionally to risk levels rather than applying uniform review to all commits.
Data Source
AI summary
Systems, methods, and software can be used to detect anomalies in source code. In some aspects, a method comprises: obtaining, by a server, one or more attribute values associated with one or more code commits of source code; and generating, by the server and based on the one or more attribute values, an anomaly report indicating a risk level of the source code.