Secure Intelligent Things: Advances in AI-Driven Cybersecurity and Privacy Protection for IoT
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 35
Special Issue Editor
Interests: privacy protection; federated learning; embodied AI; enterprise AI engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The exponential proliferation of the Internet of Things has established a hyper-connected ecosystem where intelligent devices autonomously govern critical infrastructure, smart cities and healthcare systems. As we advance toward 2026, the convergence of Artificial Intelligence and Internet of Things—often termed AIoT—has become the linchpin of digital transformation. However, this ubiquity introduces profound security challenges. The massive scale of connected devices expands the attack surface, while their limited computational resources make them vulnerable to sophisticated threats, ranging from Artificial Intelligence-driven polymorphic malware and adversarial evasion attacks to privacy breaches in data-rich sensing environments. Traditional, static security mechanisms are no longer sufficient to protect these dynamic, heterogeneous networks.
This Special Issue, "Secure Intelligent Things: Advances in AI-Driven Cybersecurity and Privacy Protection for IoT," aims to consolidate cutting-edge research that leverages Artificial Intelligence as a proactive defense mechanism. We seek to explore how advanced learning paradigms—including Deep Learning, Reinforcement Learning and Federated Learning—can be deployed at the network edge to enable autonomous threat detection, self-healing networks and robust identity management. Simultaneously, we address the "security of Artificial Intelligence" itself, inviting contributions on adversarial robustness, model explainability and privacy-preserving frameworks such as Homomorphic Encryption and Blockchain-based decentralized trust. This collection will bridge the gap between theoretical Artificial Intelligence advances and practical, scalable security architectures for the next generation of intelligent things.
Dr. Yan Huang
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence-driven cybersecurity
- privacy-preserving federated learning
- adversarial machine learning
- edge intelligence
- zero trust architecture
- blockchain for Internet of Things
- intrusion detection systems
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