ntop/nDPI
Oct 2024 – Oct 2024
1 Month active
Languages Used
CSVMakefile
Technical Skills
Build SystemsData EngineeringMachine LearningNetwork SecurityTesting Infrastructure
YellowMan
PROFILE
Yellowman
Developed and integrated a machine learning-ready DGA detection testing dataset and supporting test infrastructure for the ntop/nDPI repository, enabling robust evaluation of ML models targeting DGA-based network threats. Curated a comprehensive set of legitimate and DGA domains, incorporated into the ml_tests suite to improve testing coverage and reproducibility. Leveraged data engineering and testing infrastructure skills to streamline the packaging and deployment process by introducing a Makefile and updating distribution tooling. Utilized CSV and Makefile languages to ensure the DGA test assets are easily deployable, ultimately strengthening the repository’s ML-based threat detection capabilities and supporting reproducible research and development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
676,026
Activity Months1
Work History
October 2024
2 Commits • 1 Features
Oct 1, 2024Summary for 2024-10: Delivered ML-ready DGA detection testing dataset and test infrastructure for ntop/nDPI, enabling robust evaluation of ML models for DGA-based traffic detection. Implemented a comprehensive dataset of legitimate and DGA domains integrated into the ml_tests suite, and added a Makefile to the distribution tooling to package the DGA test infrastructure (EXTRA_DIST) for easier deployment. No major bugs fixed this month. Overall impact: strengthened ML-based threat detection capabilities, improved testing coverage and reproducibility, and streamlined packaging/deployment of DGA testing assets. Technologies/skills demonstrated include ML-driven testing data curation, test infrastructure development, Makefile-based tooling, and distribution packaging.
2 Commits • 1 Features
Oct 1, 2024Summary for 2024-10: Delivered ML-ready DGA detection testing dataset and test infrastructure for ntop/nDPI, enabling robust evaluation of ML models for DGA-based traffic detection. Implemented a comprehensive dataset of legitimate and DGA domains integrated into the ml_tests suite, and added a Makefile to the distribution tooling to package the DGA test infrastructure (EXTRA_DIST) for easier deployment. No major bugs fixed this month. Overall impact: strengthened ML-based threat detection capabilities, improved testing coverage and reproducibility, and streamlined packaging/deployment of DGA testing assets. Technologies/skills demonstrated include ML-driven testing data curation, test infrastructure development, Makefile-based tooling, and distribution packaging.
October 2024
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVMakefile
Technical Skills
Build SystemsData EngineeringMachine LearningNetwork SecurityTesting Infrastructure
Repositories Contributed To
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