Brian Wylie is a seasoned information-security engineer and data scientist with 12+ years building scalable analysis and visualization tooling, currently leading projects under his own SuperCowPowers LLC. He has driven open-source security work at Kitware and contributed substantive protocol-parsing improvements to the well-known dpkt packet library, improving Ethernet, DNS and HTTP handling. His background spans national lab research at Sandia through hands-on roles at Mandiant, Vectra, and Click Security, where he applied statistics and machine learning to real-world threat detection problems. Comfortable across the stack, he combines low-level packet and protocol expertise with machine-learning workflows in Python (Pandas/Scikit-learn) for PE file classification and other security datasets. Based in Albuquerque, he brings a practical research-to-production mindset and a taste for unconventional perspectives—his GitHub bio hints at a playful, systems-level curiosity that informs his engineering.
fast, simple packet creation / parsing, with definitions for the basic TCP/IP protocols
Role in this project:
Back-end Developer
Contributions:9 releases, 15 reviews, 263 commits in 7 years 4 months
Contributions summary:Brian's contributions primarily involved modifications to the core packet processing logic of the dpkt library. They made changes to various protocol definitions (e.g., Ethernet, DNS, HTTP) to improve parsing and functionality. Furthermore, the user added support for truncated DNS packets, and also made modifications to the handling of padding and trailers in Ethernet frames. The user appears to be focused on improving the library's packet analysis capabilities.
Contributions:32 commits, 2 PRs, 7 pushes in 5 years 2 months
Contributions summary:Brian primarily contributed to a Jupyter Notebook focused on PE (Portable Executable) file classification. Their work involved importing and utilizing Python libraries like Pandas and Scikit-learn. The user implemented data loading, analysis, and visualization techniques to explore and understand the PE file data, demonstrating their ability to build machine-learning workflows.
pythondata-science
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