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LLM-based Multi-class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

arXiv · Oct 30

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.

Three KAUST scientists named MIT Innovators under 35

KAUST · Dec 24

Three KAUST scientists—Hamed Albalawi, Hend Mohamed, and Walaa Khushaim—have been named MIT Technology Review Innovators Under 35 MENA. Albalawi developed a calcium carbonate ink for 3D-bioprinting coral restoration scaffolds, while Mohamed created catalysts for sustainable aviation fuel production. Khushaim developed multiplexed biosensors for early heart attack detection, integrated into portable diagnostic devices. Why it matters: This recognition highlights the growing innovation ecosystem at KAUST and the potential for Saudi Arabia to contribute significantly to global challenges in sustainability and healthcare.