KAUST researchers are developing cybersecurity solutions to protect Saudi Arabia’s critical infrastructure. This effort is highlighted by the recent CrowdStrike software update incident that caused global disruptions. KAUST is collaborating with industry partners to translate research into practical solutions, focusing on securing energy systems, satellite communications, and power grids. Why it matters: Strengthening cybersecurity resilience is crucial for Saudi Arabia given its reliance on digital infrastructure and increasing sophistication of cyber threats targeting critical systems.
KAUST researchers are simulating cyberattacks on microgrids to assess their impact and develop detection/suppression methods. They used the Canadian urban distribution model with four inverter-based distributed generations (DGs) to capture system dynamics. The simulations considered attacks altering measurement data, modifying control signals, and causing sudden load changes, all of which had damaging effects. Why it matters: This research is crucial for ensuring the resilience of increasingly complex microgrids against cyber threats, especially as they become more integrated into critical infrastructure.
Munther Dahleh, director at the MIT Institute for Data, Systems, and Society (IDSS), discussed his group's research on network systems at the KAUST 2018 Winter Enrichment Program. The research focuses on the fragility of large networked systems, like highway systems, in response to disruptions that may lead to catastrophic failures. Dahleh's team studies transportation networks, electrical grids, and financial markets to understand system interconnection in causing systemic risk. Why it matters: Understanding networked systems is crucial for building resilient infrastructure and mitigating risks in critical sectors across the GCC region.
Marcus Engsig at DERC has developed DomiRank, a new centrality metric to quantify the dominance of nodes within networks. DomiRank integrates local and global topological information to determine the importance of each node for network stability. The research demonstrates that nodes with high DomiRank values indicate vulnerable areas heavily dependent on dominant nodes. Why it matters: This metric can help identify critical infrastructure components and vulnerabilities in complex systems, enhancing resilience against targeted attacks.
MBZUAI researchers are applying federated learning to optimize smart grids while protecting user data privacy. This approach leverages techniques from smart healthcare systems to enhance energy efficiency and local energy sharing. The research addresses the challenge of balancing grid optimization with the risk of user identity theft associated with traditional data-intensive smart grids. Why it matters: This research demonstrates a practical application of privacy-preserving AI in critical infrastructure, addressing key concerns around data security and fostering trust in smart grid technologies.
KAUST hosted the "Human-Machine Networks and Intelligent Infrastructures" conference, co-organized by Prof. Jeff Shamma and Asst. Prof. Meriem Laleg. The conference explored the blend of engineered devices and human elements in large-scale systems like smart grids. Keynote speaker Dr. Pramod Khargonekar discussed cyber-physical-social systems and emerging trends. Why it matters: The conference highlights the growing importance of understanding the interplay between AI, infrastructure, and human behavior in the development of smart cities and intelligent systems in the region.
This paper introduces an AI-driven decision support system for green hydrogen investment in Oman, specifically for the Duqm R3 auction. The system uses publicly available meteorological data to predict maintenance pressure on hydrogen infrastructure, creating a Maintenance Pressure Index (MPI). This tool supports regulatory oversight and operational decision-making by enabling temporal benchmarking against performance claims.