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Cybersecurity and Trustworthiness in IoT Devices

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (26 May 2026) | Viewed by 779

Special Issue Editors

Department of Convergence Security Engineering, Sungshin Women’s University, Seoul 02844, Republic of Korea
Interests: information security; communications and networks; IoT; security and privacy; machine learning; artificial intelligence
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Guest Editor
Department of Electronics Engineering and Applied Communications Research Center, Hankuk University of Foreign Studies (HUFS), Yongin 17035, Republic of Korea
Interests: terahertz communications; satellite communications; covert communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid proliferation of Internet of Things (IoT) devices across critical domains—such as industrial automation, healthcare systems, smart grids, and autonomous transportation—has brought transformative benefits through automation, connectivity, and real-time data analytics. However, these advantages come with heightened cybersecurity and trustworthiness challenges, due to the inherent limitations in IoT device resources, the heterogeneity of platforms, and their exposure to persistent connectivity.

This Special Issue aims to present state-of-the-art research that addresses the multi-dimensional security and trust challenges in IoT ecosystems. We welcome original contributions that explore novel frameworks for system protection, including lightweight encryption schemes, secure firmware update mechanisms, privacy-preserving protocols, anomaly detection models, and AI/ML-driven threat identification. Papers focusing on hardware-based security, zero -trust architectures, and trust evaluation models are also encouraged, especially those applied to real-world systems in high-stakes environments such as critical infrastructure and medical devices.

In addition, the Special Issue seeks submissions that contribute to the benchmarking, standardization, and regulatory alignment of IoT security practices—highlighting the importance of developing practical, interoperable, and scalable solutions. Studies offering experimental validations, real-world deployment case studies, and curated datasets for the broader research community are especially welcome.

To help set a foundation for the Special Issue, we also invite review articles and position papers that critically assess current trends, challenges, and future directions in IoT security and trustworthiness. By bringing together insights from academia, industry, and policymakers, this Special Issue aims to foster interdisciplinary dialogue and promote best practices for the secure and ethical deployment of IoT systems.

Dr. Il-Gu Lee
Dr. Jung Hoon Lee
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoT
  • cybersecurity
  • trustworthiness
  • AI/ML
  • privacy
  • zero trust
  • policy
  • regulation
  • standardization

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Published Papers (1 paper)

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Research

30 pages, 14138 KB  
Article
Self-Evolving Multi-Agent Fuzzing for Industrial IoT with Knowledge-Driven Cognitive Reasoning
by Bowei Ning, Xuejun Zong, Kan He, Guogang Wang, Lian Lian, Yifei Sun and Jinyang Liu
Sensors 2026, 26(11), 3348; https://doi.org/10.3390/s26113348 - 25 May 2026
Viewed by 233
Abstract
Securing the Industrial Internet of Things (IIoT) is paramount, yet proprietary protocols remain vulnerable to deep-state logic flaws that traditional fuzzers often fail to reach. We propose MALF, a Multi-Agent LLM Fuzzing Framework that couples a dynamic Industrial Security Knowledge Graph (ISKG) with [...] Read more.
Securing the Industrial Internet of Things (IIoT) is paramount, yet proprietary protocols remain vulnerable to deep-state logic flaws that traditional fuzzers often fail to reach. We propose MALF, a Multi-Agent LLM Fuzzing Framework that couples a dynamic Industrial Security Knowledge Graph (ISKG) with collaborative cognitive agents for effective, efficient, and trustworthy IIoT security testing. A self-evolving knowledge loop mitigates LLM hallucinations by grounding the generation in verifiable graph constraints; QLoRA-tuned models aligned with hexadecimal features enable low-latency mutation; and Chain-of-Thought reasoning reconstructs protocol states for intent-driven attacks. On a heterogeneous testbed spanning five industrial protocols and ten vendors, MALF achieves an average Test Case Acceptance Rate of 88.3% (peak 91.2% on Modbus/TCP) and 91.2% ISKG-defined state coverage, outperforming rule-based, RL-based, and LLM baselines. On a 15-vulnerability N-Day benchmark, MALF detects all known cases, against 60%, 47%, 40%, and 27% for NCMFuzzer, MARLFuzz, BooFuzz, and Fuzz4All, respectively. In a separate real-world campaign, MALF further identifies 14 previously unknown vulnerability candidates, of which four have been assigned CNVD identifiers (CNVD-2024-16009, CNVD-2025-22875, CNVD-2025-29811, CNVD-2026-06041) and 10 remain under vendor review. These results provide controlled-testbed evidence that knowledge-grounded AI agents can systematically expose deep-state vulnerabilities in opaque IIoT environments. Full article
(This article belongs to the Special Issue Cybersecurity and Trustworthiness in IoT Devices)
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