Working at the intersection of ML and security to uncover hidden patterns in noisy data and move detection toward predictive intelligence.

ML for Security
Building end-to-end machine learning pipelines for log analysis, including parsing, enrichment, feature engineering, and baseline model evaluation. Focused on identifying subtle patterns and behaviors that traditional tools miss.

Threat Intelligence Analysis
Collecting, organizing, and correlating threat intelligence with observed indicators and cybersecurity-relevant data to strengthen understanding of emerging threats and support predictive detection approaches.

Security Data Analysis
Analyzing system and network logs to identify anomalies and technical indicators using structured, repeatable workflows designed to separate noise from actionable signals.

Projects reflecting my focus on machine learning, data workflows, and security analysis.

Cyber Threat Analyzer (CTA)
A reproducible machine-learning pipeline built to detect brute-force activity using structured log parsing, enrichment, feature engineering, and baseline modeling. Designed to demonstrate how data-driven workflows improve traditional analysis and provide a foundation for future predictive detection work.

bobgaynor-dev
A modern, responsive portfolio website showcasing my work at the intersection of security and machine learning.

ML & Deep Learning Foundations
A unified repository combining foundational machine-learning work with early deep-learning experiments.

Python · Jupyter · Conda · Pandas · NumPy · scikit-learn

CI/CD workflow with GitHub Actions automating builds and updates for bobgaynor-dev.