Collaborative Research: EAGER: SaTC-EDU: Dynamic Adaptive Machine Learning for Teaching Hardware Security (DYNAMITES)
Cybersecurity is key to safeguarding societal wellbeing in the present digital era. As threats at the hardware level become more prevalent, skills and knowledge for hardware security become more crucial for cybersecurity professionals. In addition, the rise of artificial intelligence (AI) promises to rapidly evolve the threat landscape. To prepare the next-generation cybersecurity workforce, students need opportunities to hone their skills on a variety of different hardware security problems. Current curriculum on hardware security focuses on theory and a small number of hand-crafted exercises, thus providing limited opportunity to apply learning to evolving scenarios. To address these drawbacks, this project intertwines AI and hardware security to develop new tools for preparing students to be creative and flexible, and ultimately, better prepared for dealing with newly emerging hardware security threats. To improve the state-of-the-art in hardware security and cybersecurity education, this project is seeking new insights at uncharted intersections of hardware security and AI-based decision making. The project will investigate how to enable students to attack and defend hardware by sparring against DYNAMITES, which is a dynamic adaptive machine learning tool for teaching hardware security. The project will also examine hardware security pedagogy to understand the impact of the tool in shaping students? cognitive processes. The major goal is to develop and evaluate DYNAMITES through research in three directions: (1) investigating and adapting techniques to allow AI to understand hardware, (2) exploring how AI can be used to produce new problems intelligently, and (3) exploring how AI in the learning environment affects the "security mindset" in students. These findings will allow hardware security education to scale, reducing the barrier to entry and arming future professionals with the skills needed to protect critical systems, as well as jump-starting innovations in automated, scalable scanning and patching of hardware vulnerabilities. The hardware attack/defense artifacts emerging from this project will be released for use in teaching and research, and the project team will disseminate tools/techniques that emerge from this project. 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.