AI-Driven Cybersecurity, Resilience, and Trust Frameworks for Future Urban IoT Systems

Dear Colleagues,

Urban Internet of Things (IoT) systems — spanning smart transportation networks, connected and autonomous electric vehicles, intelligent energy grids, environmental monitoring infrastructures, and large-scale cloud and edge computing platforms — are becoming essential to the daily operation and sustainability of modern cities. These systems enable real-time decision-making, automation, and service optimization, yet they face an expanding spectrum of sophisticated cyber threats, ranging from large-scale distributed attacks to stealthy, AI-generated intrusions.

Ensuring the cybersecurity, resilience, and trustworthiness of these infrastructures requires innovative strategies that safeguard both the digital and physical layers of urban services. Traditional security mechanisms are no longer sufficient in the face of emerging attack vectors, such as adversarial AI, supply chain compromises, and quantum-era cryptographic threats. The shift toward zero-trust architectures, privacy-preserving computation, and autonomous threat response is now critical for sustaining uninterrupted and secure service delivery.

Artificial Intelligence (AI) and Machine Learning (ML) are central to this transformation, powering intrusion detection, anomaly detection, and predictive threat intelligence. However, these solutions often face challenges in resource-constrained environments — including edge devices, embedded IoT platforms, and in-vehicle systems — where energy efficiency, low latency, and real-time adaptability are paramount. Addressing these demands calls for hardware–software co-design, including FPGA and ASIC accelerators, power-aware AI model deployment, and digital twin-based security testing for proactive vulnerability assessment.

This Special Issue of Electronics invites original research and reviews on advanced methods, architectures, and frameworks that enhance the cybersecurity, resilience, and trust of urban IoT ecosystems. Contributions may address secure communication protocols, post-quantum cryptographic solutions, resilient and fault-tolerant system design, federated and distributed learning for IoT security, and real-time autonomous response systems.

Topics of interest include, but are not limited to:

• AI/ML-based intrusion detection, anomaly detection, and predictive threat intelligence for urban IoT;
• Zero-trust architectures and continuous authentication mechanisms for urban infrastructures;
• Post-quantum cryptography and lightweight encryption for IoT and IIoT devices;
• Hardware–software co-design for secure, energy-efficient, and real-time IoT systems;
• FPGA and ASIC accelerator designs for AI-based security and privacy applications;
• Adversarial machine learning defenses and robust AI for IoT cybersecurity;
• Digital twin frameworks for urban IoT security testing, simulation, and resilience analysis;
• Privacy-preserving and federated learning methods for multi-domain IoT security;
• Resilient architectures for connected and autonomous vehicle systems;
• Low-power, real-time edge computing platforms for critical urban services;
• Secure and sustainable cloud/edge data centers for large-scale IoT workloads;
• Dataset creation, benchmarking, and evaluation for next-generation IoT security research.

By integrating cutting-edge AI-driven security, energy-efficient hardware acceleration, and resilience-focused architectures, this Special Issue aims to advance the creation of scalable, sustainable, and secure urban IoT systems capable of withstanding evolving cyber threats while maintaining public trust and operational continuity.

Dr. Jake Cho
Dr. Victoria Kim
Guest Editors

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• urban Internet of Things (IoT)
• cybersecurity
• resilience
• zero-trust architecture
• post-quantum cryptography
• adversarial machine learning
• hardware–software co-design
• FPGA accelerators
• ASIC accelerators
• energy-efficient AI
• intrusion detection
• anomaly detection
• federated learning
• digital twin
• connected vehicles
• smart transportation
• sustainable computing
• privacy-preserving AI