AI-Enhanced Cybersecurity for Critical Infrastructure Using Digital Twins

The rapid digitization of critical infrastructure, ranging from energy grids and water treatment facilities to urban transportation and smart city systems, has created unprecedented vulnerabilities. Cyber-physical attacks can cascade into severe disruptions, posing risks to public safety and national security. While traditional cybersecurity approaches focus on reactive defenses, there is a growing need for predictive, adaptive, and AI-driven strategies. Digital twins, which are high-fidelity virtual representations of physical systems, offer a unique opportunity to simulate, monitor, and analyze infrastructure behavior in real time, enabling proactive threat detection and mitigation. Integrating AI with digital twins allows continuous learning of system patterns, empowering infrastructure managers to anticipate and respond to complex cyber threats before they manifest physically.



This Research Topic aims to advance AI-enhanced cybersecurity strategies for critical infrastructure using digital twins. We seek contributions that explore how real-time simulations of cyber-physical systems can identify vulnerabilities, forecast potential attack impacts, and inform automated defense mechanisms. Topics of interest include the deployment of AI-driven anomaly detection systems that learn baseline infrastructure behavior, predictive modeling of ransomware or malware propagation across networks, and frameworks for proactive isolation or mitigation of threats. By bridging AI, cybersecurity, and digital twin technologies, this Research Topic intends to provide actionable insights for securing smart cities, industrial control systems, and municipal infrastructures. Authors are encouraged to present case studies, pilot projects, or theoretical frameworks demonstrating measurable improvements in resilience, safety, and operational continuity in critical systems under cyber threat conditions.



We invite contributions from researchers, practitioners, and policy experts focusing on the intersection of AI, cybersecurity, and digital twins. Manuscripts may cover, but are not limited to, topics such as AI-based anomaly detection for cyber-physical systems, simulation of cyber-attacks on critical infrastructure, risk assessment frameworks, urban resilience strategies, and proactive defense mechanisms for smart city infrastructures. Submissions can include original research articles, review papers, short communications, or methodological studies that demonstrate practical applications, pilot studies, or computational frameworks. Authors should provide actionable insights, innovative methodologies, or lessons learned from deployments in real or simulated critical infrastructures. Contributions that bridge interdisciplinary perspectives and provide measurable cybersecurity outcomes are especially encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: AI-Enhanced Cybersecurity, Digital Twins, Critical Infrastructure Protection, Smart cities Security, Cyber-Physical Systems, Anomaly Detection, Ransomware Simulation, Urban Resilience, Proactive Defense Frameworks, Infrastructure Risk Modeling

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.