Artificial Intelligence for Securing Critical Infrastructure and IoT Systems

The rapid growth of interconnected systems and Internet of Things (IoT) devices has transformed critical infrastructures such as smart grids, healthcare, transportation, and industrial control systems. While these technologies improve efficiency and scalability, they also introduce complex cybersecurity risks. Traditional security approaches are no longer sufficient to defend against sophisticated and adaptive threats. Artificial Intelligence (AI) has emerged as a key solution, offering advanced capabilities such as anomaly detection, predictive threat intelligence, automated response, and enhanced system resilience through techniques like machine learning and deep learning. However, integrating AI into these environments presents challenges, including adversarial attacks, data privacy concerns, limited explainability, and resource constraints of IoT devices. Ensuring the robustness, trustworthiness, and scalability of AI-based security solutions remains an ongoing challenge. This article collection aims to gather cutting-edge research on AI-driven cybersecurity for critical infrastructure and IoT, highlighting innovative methods, practical applications, and future research directions.

The primary goals of this Research Topic are:

• To advance the state-of-the-art in AI-driven cybersecurity for critical infrastructure and IoT systems
• To explore innovative and scalable AI techniques for detecting and mitigating cyber threats
• To address key challenges such as trustworthiness, robustness, and explainability of AI models
• To foster interdisciplinary collaboration between academia, industry, and policymakers
• To highlight practical implementations and real-world use cases

This Research Topic welcomes original research articles, reviews, and case studies addressing (but not limited to) the following topics:

• AI-based intrusion detection and anomaly detection in IoT and critical infrastructure
• Machine learning for malware detection and analysis in embedded and IoT devices
• AI-driven threat intelligence and predictive analytics for cyber-physical systems
• Security of industrial control systems (ICS) and SCADA using AI techniques
• Deep learning approaches for network traffic analysis in IoT environments
• Federated learning and distributed AI for secure IoT ecosystems
• Privacy-preserving AI techniques in critical infrastructure security
• Adversarial attacks and defenses in AI-enabled IoT systems
• Explainable and trustworthy AI for cybersecurity applications
• AI-based risk assessment and vulnerability management
• Edge AI for real-time security monitoring
• Blockchain and AI integration for securing IoT networks
• Case studies and real-world deployments in smart cities, healthcare, and energy systems