I enjoy making things. Here are a selection of projects that I have worked on over the years.
The goal of this project is to invent creative ways of deciphering cybersecurity data with ML and AI. First, exploratory data analysis is used with traditional Python libraries and Generative AI models to get insights on black box data, visualize it and tell its story. Then several traditional ML techniques for feature engineering are used to derive useful data characteristics for detecting malicious behavior. Finally, ML classification and language models are fine tuned using the generated features for detection of anomalies in network traffic.
We cannot improve what we cannot measure (Kelvin). How does one quantify the absence of an adversary, a fault, or a weakness in a system? How can we define objective, repeatable, and reproducible metrics that evaluate the security of a network? This task is more complex at a time that the face of networking is changing by interconnecting devices such as webcameras, locks, and lights. These exciting and difficult questions are part of my work in quantitative security evaluation. I analyze data, create predictive models, and stream telemetry with modern open source tools.
