Malicious Software Detection via Ensemble ML Analysis
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Solution Overview
Problem
Current methods for detecting malicious software packages in software supply chains are inadequate, particularly in open-source environments, as they often rely on manual analysis and lack an ensemble learning approach to identify malicious code and suspicious community behavior, leading to false positives and limited coverage across various programming languages and obfuscation techniques.
Innovation Solution
The implementation of an ensemble learning approach combining code-level models and community-level models using machine learning to detect malicious software components and suspicious behavior, integrating with search engines, software composition analysis tools, and continuous integration/continuous deployment (CI/CD) pipelines to block malicious packages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis methods are used to detect malicious software packages, then detection accuracy may be maintained with simple tools, but productivity is reduced and false positives increase
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine learning systems. Multiple ML models (code-level models, community-level models, ensemble models) automatically analyze software packages, replacing human analysts and dramatically increasing detection speed while maintaining or improving accuracy through algorithmic consistency and pattern recognition capabilities.
Solution Approach 2:
The patent employs an ensemble learning approach that combines multiple different ML models (code analysis models, community behavior models, suspicious pattern models) into a composite detection system. This composite approach leverages the strengths of each individual model to achieve higher overall detection accuracy and reduce false positives compared to any single model alone.
2Adaptability or versatility
If comprehensive analysis of code and community data is performed, then detection coverage improves, but device complexity increases
Solution Approach 1:
The patent segments the detection system into distinct modular components: code-level analysis models, community-level analysis models, data collection modules, and ensemble aggregation modules. Each module handles a specific aspect of the analysis, making the overall complex system manageable, maintainable, and scalable while achieving comprehensive detection coverage across multiple dimensions.
3Reliability
If multiple machine learning models are combined for ensemble learning, then false positives are reduced, but measurement precision requirements increase
Solution Approach 1:
The patent merges multiple ML model predictions through an ensemble learning framework that aggregates results from code-level models, community-level models, and suspicious pattern models. This combination approach reduces false positives by requiring consensus across multiple independent analysis dimensions, with each model contributing its specialized detection capabilities to the unified decision-making process.
Data Source
AI summary
Embodiments of the disclosure provide systems and methods for detecting malicious software packages. Detecting malicious software packages can include collecting information identifying one or more known malicious software component classifiers, collecting information identifying one or more known suspicious community behavior classifiers associated with the one or more known malicious software component classifiers and receiving a software package including software components. The method also includes identifying one or more software components of the software package as malicious based on a comparison between the software components of the software package and each of the collected one or more known malicious software component classifiers and the collected one or more known suspicious community behavior classifiers, generating a malicious probability for each of the identified one or more software components and evaluating whether the software package is malicious based on the generated malicious probability for each of the identified one or more software components.