Dr. Zhiqiang Lin from Ohio State University presented the Security-Enhanced Radio Access Network (SE-RAN) project to address cellular network threats using O-RAN. The project includes 5G-Spector, a framework for detecting L3 protocol exploits via MobiFlow and MobieXpert, and 5G-XSec, a framework leveraging deep learning and LLMs for threat analysis at the network edge. Dr. Lin also outlined a vision for AI convergence with cellular security for enhanced threat detection. Why it matters: Enhancing 5G security through AI and open architectures is critical for protecting next-generation mobile networks in the GCC region and globally.
Muhammad Shafique from NYU Abu Dhabi discusses building energy-efficient and robust EdgeAI systems. The talk covers trends, challenges, and techniques for optimizing software and hardware stacks. These optimizations aim to enable embodied AI in autonomous systems, IoT-Healthcare, Industrial-IoT, and smart environments. Why it matters: The research addresses key challenges in deploying AI on resource-constrained edge devices in the GCC region, particularly regarding energy efficiency and security.
The Secure Systems Research Center (SSRC) has partnered with the University of New South Wales (UNSW Sydney) to research enhancements and scaling of the seL4 microkernel on edge devices. The collaboration aims to extend the seL4 microkernel to support dynamic virtualization, combining minimal trusted computing base with strong isolation. This will address challenges related to heterogeneous hardware, software, and environmental factors in edge computing. Why it matters: This partnership aims to improve the security of edge devices in critical sectors, addressing vulnerabilities in cyber-physical and autonomous systems.
This paper introduces a framework that combines machine learning for multi-class attack detection in IoT/IIoT networks with large language models (LLMs) for attack behavior analysis and mitigation suggestion. The framework uses role-play prompt engineering with RAG to guide LLMs like ChatGPT-o3 and DeepSeek-R1, and introduces new evaluation metrics for quantitative assessment. Experiments using Edge-IIoTset and CICIoT2023 datasets showed Random Forest as the best detection model and ChatGPT-o3 outperforming DeepSeek-R1 in attack analysis and mitigation.
TII's Secure Systems Research Center (SSRC) has partnered with Khalifa University, the University of Turku, and Graz University of Technology on secure mesh communications research projects. The collaborations aim to strengthen the security of Mesh network technology being developed by TII for machine-to-machine and human-to-human communications. The projects will focus on areas like communication resilience, physical layer security, and secure routing to resist eavesdropping, scanning, malware propagation, and jamming. Why it matters: This multi-party collaboration enhances the UAE's position in secure communication technologies, crucial for cyber-physical systems and autonomous operations in the region.