The cybersecurity and machine learning (ML) fields have evolved relatively independently. The occasional overlap between the two fields generally takes the form of either (1) applications of ML to statistical anomaly detection (e.g., malware detection); or (2) adversarial attacks on ML detection algorithms (e.g., adversarial ML). The cybersecurity and ML fields are also rapidly advancing, which makes education both in these respective fields and at their intersection critical. Advancement and re-skilling the United States cybersecurity workforce through large-scale, online training in data-driven and ML methods is critical for keeping the country secure and the workforce competitive. The project team will address this critical need by developing curricula for large-scale, online training of mid-career security professionals who aim to develop the skills to apply both conventional and cutting-edge ML tools to cybersecurity.
This project will develop curricula at the intersection of ML and cybersecurity with a focus on applications of ML to practical, real-world security use cases. In addition, the project will establish a pedagogical foundation for security researchers to evaluate and apply various potential ML-based approaches to cybersecurity. The project is focused, in particular, on training mid-career professionals who have a classical training in cybersecurity (and thus an understanding of practical concepts), but need to gain a stronger foundation in data-driven methods that have become the basis for most applied cybersecurity in the past decade. The project outcomes will include: (1) online curricular development in data-driven security, to provide mid-career professionals foundations and practical tools for applying these methods to practical problems in network security; (2) formative research to elicit desired skills and use cases from the workforce; (3) modular public toolkits and datasets for use in both courses and as resources for professionals to apply in practical settings; and (4) augmented teaching materials, tailored to individual students, based on intelligent tutoring systems.
This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
