Collaborative Research: SaTC: EDU: Adversarial Malware Analysis - An Artificial Intelligence Driven Hands-On Curriculum for Next Generation Cyber Security Workforce

Tennessee Technological University, Cookeville TN

Investigators

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) techniques can bolster cybersecurity by aiding security administrators in detecting suspicious behaviors and initiating responses to threats. However, AL/ML technology remains susceptible to malicious exploitation, potentially leading to unintended outcomes. Therefore, it is important to ensure that AI-based decision processes are reliable in critical operational systems when facing adversarial situations. As deep learning (DL) and other AI/ML algorithms become integrated into operational systems, it is essential to defend security, privacy, and fairness of AI/ML against adversaries. This can be achieved by implementing more robust ML methods such as AI reconnaissance prevention, analysis of adversarial models, model poisoning prevention, and secure training procedures. By equipping students with the knowledge needed to secure AI in malware analysis applications, this project will foster growth of next-generation cybersecurity talent. This project will research and develop self-contained course modules focused on Adversarial Machine Learning (AML) within the context of malware analysis applications, which will transit cutting-edge research topics into the teaching and learning process. The goal of these modules is to develop students at Tennessee Tech University (TTU) and North Carolina Agricultural and Technical State University (NCAT) with specialized knowledge in this area. Course modules will include adversarial malware generation, robustness of file structure against random perturbation, poisoning attack and defense, white-box evasion attack, and surrogate model construction. The AML cyber modules will be integrated into different non-security courses such as AI/ML or data science or provided as an independent cybersecurity course. Students will acquire practical and conceptual knowledge by engaging with different AI/ML techniques for security solutions pertinent to the malware analysis domain. Additionally, students will develop advanced skills necessary for safeguarding AI systems. The interdisciplinary team, composed of experts in cybersecurity, artificial intelligence, and education, will utilize a guiding conceptual framework to strategically develop cybersecurity education modules. They will investigate the impact of these modules on learning outcomes, while refining pedagogical strategies to promote diversity and inclusion in cybersecurity education. Developed modules, instructional materials, and tutorial activities will be widely available for dissemination. This project will support integration of security and education research topics to create new knowledge in cybersecurity. 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.