Forging New Paths in Cybersecurity Doctoral Research with Open Datasets and Synthetic Data Generation

This research-to-practice full paper addresses the important need for relevant and comprehensive datasets to advance cybersecurity research by proposing methods for curating open datasets and generating synthetic datasets. Cybersecurity research is a rapidly evolving scientific field, making robust datasets crucial for empirical analysis. Unfortunately, current doctoral research is hindered by the scarcity, limited accessibility, and outdated or irrelevant nature of existing open-source datasets. This paper tackles these challenges by focusing on two main initiatives: (1) curating a pilot collection of open datasets aligned with the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework, and (2) using Generative Adversarial Networks (GANs) to generate synthetic datasets. Our research highlights the obstacles faced by doctoral students due to fragmented, outdated data and underscores the importance of accessible datasets for rigorous scientific inquiry. We also demonstrate how synthetic data can ease privacy concerns while still offering researchers realistic data. By incorporating these approaches into doctoral curricula, we aim to equip future cybersecurity researchers with the skills resources for impactful research. The authors will continue to expand their dataset curation efforts and study how discoverable, high-quality datasets can influence doctoral research, particularly empirical studies and their outcomes.