Valderrama, S. (CSE) – Closing Data Gaps for Cybersecurity AI Training and Evaluation

Artificial intelligence and machine learning models depend on large and diverse datasets to learn patterns across different situations and generalize to cases they have not seen before. In cybersecurity, suitable data is difficult to find for two main reasons: first, sensitive information limits what organizations can share; second, public datasets often lack the coverage or context in specific use cases. My research addresses this problem by developing methods that synthetically create and curate data, where we propose data-generation algorithms that provide mechanisms to generate data to close these gaps.

So far, we have started this research by developing algorithms to curate data for specific cybersecurity needs. We collect and label a collection of 64,132 command blocks from different data sources trying to describe the security behavior they represent. Measuring this collection reveals important differences in coverage and provides a basis for directing command generation toward these rare behaviors. We then develop a generation and validation process that fills these gaps, creating new examples that are correct, relevant, and diverse. Finally, we automate the process of creating applications that could serve different cybersecurity purposes, which together with a set of instructions, a way of grading them, and reference solutions became a tasks, a valuable training unit for machine learning models.

These contributions show that we can reliably guide synthetic data generation that produces useful results. As future work, we will extend this approach from individual applications to complex environments involving multiple systems. Executions in these environments will produce records of activity linked to known actions and their outcomes, whether benign or malicious, enabling analysis and modeling of adversarial behavior. The broader goal is to make cybersecurity data generation more targeted, automatic, and useful for artificial intelligence research.

Event Host: Sergio Valderrama, Ph.D. Student, Computer Science & Engineering 

Advisor: Alvaro Cardenas 

Zoom: https://ucsc.zoom.us/j/92072651510?pwd=UB55FkjUfMi0vssfcqIbYsPWjDr9xw.1

Passcode: 026956