Blake Anderson
Principal Security Research Engineer at Cisco
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBlake Anderson is a Principal Security Research Engineer based in San Jose with a decade of experience at the intersection of machine learning and network/cloud security. At Cisco he leads research and productization of ML-driven features for encrypted traffic analysis, threat detection, and large-scale network forensics. His background includes an ML-focused PhD and earlier R&D at Los Alamos National Laboratory where he prototyped malware-analysis algorithms and co-led grant-funded automation of reverse engineering. Blake contributes to open-source network analysis tooling—implementing a Julia neural-network classifier for Cisco’s Joy flow-analysis project—and bridges academic rigor with production engineering. He has a knack for translating subtle protocol signals (e.g., TLS cipher ordering) into reliable security signals and for accounting for labeled-data bias when moving models from lab to real-world traffic. Colleagues rely on him to blend theoretical ML insights with practical, scalable systems that improve operational security.
10 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at The University of New Mexico
Github Skills (8)
neural-network10
machine-learning10
backpropagation10
julia10
data-analysis9
python8
apidoc7
api7
Programming languages (3)
Github contributions (5)
cisco/joy
Jan 2016 - Nov 2019
A package for capturing and analyzing network flow data and intraflow data, for network research, forensics, and security monitoring.
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:152 commits, 8 PRs, 139 pushes in 3 years 10 months
Contributions summary:Blake primarily contributed to the development of a Julia-based neural network classifier for network flow data analysis. Their work involved implementing a ReluMax neural network model, including functions for forward propagation, backpropagation, and gradient calculation. The user also integrated the classifier with a Python-based server, modified existing code, and updated the codebase for the Prague NN classifier.
analyzingcapturingnetwork-flowsecurityanalysis
cisco/mercury
Sep 2019 - Nov 2022
Mercury: network metadata capture and analysis
Contributions:489 commits, 2 PRs, 131 pushes in 3 years 3 months
bpfextractionpacket-capture-and-analysismetadatatraffic
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.