AI-Driven Autonomous Cybersecurity Solutions for IoT

Dear Colleagues,

The exponential growth of the Internet of Things (IoT) has created an interconnected ecosystem that is increasingly exposed to dynamic and intelligent cyber threats. Traditional static defenses are no longer sufficient to protect heterogeneous, large-scale IoT infrastructures. This Special Issue focuses on AI-driven autonomous cybersecurity, emphasizing the role of large language models (LLMs), agentic AI systems, and adaptive learning frameworks in securing IoT environments. We welcome contributions that explore novel AI-enabled paradigms for autonomous threat detection, intelligent response orchestration, and predictive risk assessment. Topics of interest include federated and reinforcement learning for distributed defense, multi-agent collaboration for attack mitigation, LLM-based reasoning for anomaly interpretation, and hybrid architectures that integrate explainability, privacy preservation, and real-time decision-making.

This collection aims to bridge foundational research and applied innovation, showcasing how AI agents can autonomously perceive, reason, and act to secure complex IoT systems. By uniting advances in intelligent automation, trustworthy AI, and cybersecurity governance, the Special Issue will chart a path toward resilient and self-defending digital ecosystems.

Dr. Abbas Yazdinejad
Prof. Dr. Ying Tan
Guest Editors

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