CAREER: Designing Domain Knowledge-Guided Learning Architectures towards Wireless Network Security: Enabling Effective and Efficient Attacks and Countermeasures

Modern wireless networks have become increasingly complex and densely populated and can create a massive volume of operation data. As a result, extensive efforts from both academia and industry have focused on leveraging artificial intelligence (AI) in wireless network security related tasks such as (i) adversarial inference, (ii) adversarial generation, and (iii) data transformation. This project will focus on exploring new directions for incorporating additional domain knowledge to improve the efficiency of machine learning architecture design. The project's novelties are (i) investigating the wireless-domain knowledge used in mobile network design across different protocol layers and classifying this knowledge based on how it can be deterministically incorporated into learning model design; and (ii) designing specialized learning architectures that translate this domain knowledge into AI-friendly representations to improve both learning efficiency and performance. The project's broader significance and importance are advancing the state of the art in wireless network security, enhancing undergraduate student training opportunities, openly disseminating training materials, and carrying out outreach activities. This project targets three major categories of learning models: (i) typical centralized learning models, (ii) decentralized learning models, and (iii) large language models, and explores the incorporation of wireless-domain knowledge into each class to address different security tasks. Specifically, the project outlines three research thrusts based on both system design and practical evaluations: (i) creating a new training-efficient, wireless-specific learning model as a surrogate model for resource allocation attacks; (ii) developing a new large language model-powered multi-agent framework to enable attacks in cooperative spectrum sensing; (iii) designing a novel client-to-server parameter sharing strategy in federated learning to defend against membership inference attacks. This project will also perform comprehensive evaluations based on real-world wireless experiments to validate and improve the proposed designs. 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.