Code Repository Intrusion Detection via Developer Profiling
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Solution Overview
Problem
Conventional systems face challenges in detecting malicious code intrusions into code repositories, as existing methods are expensive, time-consuming, and often ineffective, especially with large or complex source code, and may only be undertaken after a problem has been detected or deployed.
Innovation Solution
The system employs machine learning, statistical inference, and Bayesian filtering to analyze developer coding characteristics, creating profiles that can identify potential intrusions by comparing submitted code against trusted developer profiles, using features such as keystroke sequences, variable naming conventions, and temporal patterns to determine the likelihood of authorship.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of committed code patches is used, then detection accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between code submission and manual review. This system uses machine learning models trained on developer-specific coding patterns to automatically analyze submitted code patches and generate confidence scores about authorship authenticity. The intermediary filters out clearly authentic code before it reaches manual reviewers, significantly reducing their workload while maintaining high detection accuracy for suspicious submissions.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. Machine learning models analyze coding characteristics such as keystroke sequences, variable naming conventions, and temporal patterns to automatically detect potential intrusions. This substitution eliminates the time-consuming manual examination of every code patch while maintaining or improving detection accuracy through consistent, scalable automated analysis.
2Reliability
If manual review of committed code patches is used, then detection accuracy is improved, but operational complexity and cost increase
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between code submission and manual review. This system uses machine learning models trained on developer-specific coding patterns to automatically analyze submitted code patches and generate confidence scores about authorship authenticity. The intermediary filters out clearly authentic code before it reaches manual reviewers, significantly reducing their workload while maintaining high detection accuracy for suspicious submissions.
Solution Approach 2:
The patent employs computationally efficient machine learning models that can be rapidly trained and deployed. These models analyze coding characteristics through standardized feature extraction processes, creating a scalable solution that doesn't require complex infrastructure. The system processes code patches through automated pipelines that generate quick results, replacing expensive, slow manual review processes with affordable, high-speed computational analysis.
3Measurement precision
If forensic analysis is performed after problem detection, then accurate intrusion identification is achieved, but response time is too late for prevention
Solution Approach 1:
The patent implements preliminary action by training machine learning models on each developer's coding patterns before actual code submission occurs. The system establishes baseline profiles of legitimate coding behaviors in advance, including keystroke sequences, variable naming conventions, and temporal patterns. When code is submitted, these pre-trained models immediately evaluate it against the established baselines, enabling real-time detection rather than post-event forensic analysis.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors coding patterns and provides immediate evaluation of submitted code against established developer profiles. The machine learning models generate confidence scores that provide real-time feedback on the likelihood of authentic authorship. This feedback loop enables proactive prevention by identifying suspicious submissions at the moment of submission, allowing immediate intervention before malicious code is integrated into the codebase.
Data Source
AI summary
The disclosed subject matter provides for code repository intrusion detection. A code developer profile can be generated based on characteristic features present in code composed by the developer. Characteristic features can be related to the coding propensities peculiar to individual developers and, over sufficient numbers of characteristic features, can be considered pseudo-signatures. A target code set is analyzed in view of one or more developer profiles to generate a validation score related to a likelihood of a particular developer composing a portion of the target code set. This can serve to confirm or refute a claim of authorship, or can serve to identify likely author candidates from a set of developers. Where the target code set authorship is determined to be sufficiently suspect, the code set can be subjected to further scrutiny to thwart intrusion into the code repository.