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Electronics
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Security and Privacy Challenges in Integrated IoT and Edge Systems
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Security and Privacy Challenges in Integrated IoT and Edge Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 2818

Editors

Dr. Mikail Mohammed Salim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: smart contract; pool hopping; blockchain; mining pool; machine learning; network sub-slicing; sustainable 5g environment; IoT
Dr. Abir EL Azzaoui

E-Mail Website
Guest Editor
GQAS Research Center, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: information and communication technology; computer networking; information technology; network communication; networking; network security; network technology; computer networks security security; inf

Special Issue Information

Dear Colleagues,

The convergence of the Internet of Things (IoT) and edge computing is transforming how data are processed, analyzed, and acted upon across diverse domains such as smart cities, healthcare, industrial automation, and autonomous systems. By bringing intelligence closer to the data source, IoT–edge integration enables ultra-low latency, real-time decision-making, and scalable applications. At the same time, this transformation introduces unprecedented security and privacy challenges.

IoT–edge systems are inherently decentralized, heterogeneous, and resource-constrained, which makes them particularly vulnerable to novel attack surfaces. Adversaries exploit weaknesses in communication protocols, device firmware, and distributed learning processes, while emerging issues such as generative AI-enabled cyberattacks, quantum-era vulnerabilities, and multi-modal edge intelligence further expand the landscape of risks. At the same time, ensuring resilience, sustainability, and trustworthy AI at the edge represents a next-generation research frontier.

This Special Issue aims to provide a forum for the latest advances in securing integrated IoT–edge ecosystems. We welcome contributions ranging from theoretical frameworks and algorithmic innovations to system architectures, prototypes, and real-world case studies. Our goal is to explore both foundational approaches and emerging technologies that will shape the next generation of secure, privacy-preserving IoT–edge systems.

Topics of interest include, but are not limited to, the following:

  • Lightweight cryptographic and authentication protocols for IoT–edge devices;
  • Threat modeling, risk assessment, and vulnerability analysis in IoT–edge environments;
  • Privacy-preserving machine learning and federated learning at the edge;
  • Blockchain and distributed ledger technologies for IoT–edge security;
  • Zero-trust frameworks and adaptive access control in decentralized IoT–edge systems;
  • Intrusion detection and anomaly detection using edge AI;
  • Adversarial attack detection, model poisoning, and defense in federated edge intelligence;
  • Generative AI-enabled threats and AI-driven defenses for IoT–edge networks;
  • Digital twin-based security validation and predictive intrusion modeling;
  • Self-healing and autonomous recovery mechanisms for resilient IoT–edge infrastructures;
  • Post-quantum cryptography and lightweight PQC algorithms for IoT–edge integration;
  • Zero-knowledge proofs and advanced privacy-preserving protocols for real-time IoT data;
  • Secure resource management, task offloading, and orchestration at the edge;
  • Energy–security tradeoffs and sustainable security solutions in green IoT–edge systems;
  • Security and privacy challenges in critical IoT–edge applications (healthcare, smart grid, transportation, Industry 4.0);
  • Standardization and interoperability issues for multi-vendor IoT–edge platforms.

We invite original research articles and comprehensive reviews to this Special Issue. By showcasing cutting-edge approaches and forward-looking solutions, we aim to build a strong body of knowledge that advances the security and privacy of integrated IoT–edge ecosystems.

Dr. Mikail Mohammed Salim
Dr. Abir EL Azzaoui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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 security
  • edge computing
  • privacy-preserving AI
  • federated learning
  • blockchain
  • zero-trust security
  • generative AI threats
  • digital twin security
  • post-quantum cryptography
  • cyber resilience

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Published Papers (4 papers)

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Research

29 pages, 8886 KB  
Article
Privacy-Preserving Cascaded Federated Deep Learning for Nomophobia Risk Prediction with Encrypted Masked Updates
by Md Wahidur Rahman, Rahat Khan, Mais Nijim, Waseem Al Aqqad, Yoichi Tomioka, Jungpil Shin and Mehdi Hasan
Electronics 2026, 15(11), 2431; https://doi.org/10.3390/electronics15112431 - 2 Jun 2026
Cited by 1 | Viewed by 569
Abstract
Smartphones are now deeply embedded in daily life, but excessive dependence may increase the risk of nomophobia, which is associated with anxiety, sleep disruption, and reduced productivity. Existing screening methods mainly rely on self-reported questionnaires, which are subjective and difficult to scale for [...] Read more.
Smartphones are now deeply embedded in daily life, but excessive dependence may increase the risk of nomophobia, which is associated with anxiety, sleep disruption, and reduced productivity. Existing screening methods mainly rely on self-reported questionnaires, which are subjective and difficult to scale for continuous monitoring. This study proposes a privacy-preserving federated deep learning framework for three-level nomophobia risk prediction (Normal, Mild, and Severe) using smartphone usage logs while keeping raw user data on local devices. The proposed pipeline uses a publicly available secondary dataset with 1000 original records and expands it to 100,000 records through constraint-aware synthetic augmentation. A continuous risk score is computed from standardized smartphone usage indicators and then converted into three classes using tertile-based thresholds. Several local architectures, including CNN, MLP, ResMLP, Wide & Deep, and a lightweight TabNet-style gated model, are evaluated under FedAvg. In the reported experiments, differential privacy is enabled through DP-SGD with gradient clipping and Gaussian noise. To protect update transmission, the framework applies protected update sharing through encrypted transport of masked updates. Each client masks its local update and encrypts the masked payload before transmission. This mechanism improves communication confidentiality and reduces the direct exposure of client updates. Under a fixed federated setup with five clients and 25 communication rounds, tabular models achieved near-ceiling performance on the constructed test set. The MLP achieved 99.12% accuracy, 99.12% F1-score, 0.9868 MCC, and 0.9997 AUC, while Wide & Deep achieved 98.95% accuracy, 98.95% F1-score, 0.9843 MCC, and 0.9997 AUC. In contrast, sequential models such as RNN and LSTM showed near-random performance, suggesting that the current aggregated feature representation is better suited to tabular learning than temporal modeling. These results indicate that the proposed federated pipeline can effectively learn the constructed nomophobia risk labels while preserving local data ownership. However, because the labels are derived from usage features rather than clinical or psychometric assessment, the findings should be interpreted as proof-of-concept results for constructed risk labels rather than evidence of clinical diagnostic validity. Full article
(This article belongs to the Special Issue Security and Privacy Challenges in Integrated IoT and Edge Systems)
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29 pages, 2775 KB  
Article
FADES: Adaptive Drift Estimation via Conformal Signals for Streaming Intrusion Detection
by Seth Barrett, Gokila Dorai, Lin Li and Swarnamugi Rajaganapathy
Electronics 2026, 15(10), 2114; https://doi.org/10.3390/electronics15102114 - 14 May 2026
Viewed by 545
Abstract
Machine learning-based intrusion detection systems (IDS) deployed in real-world environments frequently degrade due to concept drift, where evolving traffic patterns invalidate assumptions learned during training. This challenge is especially pronounced in Internet of Things (IoT) environments, where device behavior changes over time due [...] Read more.
Machine learning-based intrusion detection systems (IDS) deployed in real-world environments frequently degrade due to concept drift, where evolving traffic patterns invalidate assumptions learned during training. This challenge is especially pronounced in Internet of Things (IoT) environments, where device behavior changes over time due to user interaction, firmware updates, and emerging attack strategies. Prior work introduced FIRCE, a framework that integrates conformal evaluation into streaming IDS pipelines to enable uncertainty-aware drift detection and adaptive retraining. In this journal extension, we present FADES, a framework for adaptive drift estimation that generalizes drift monitoring beyond prediction-space uncertainty by supporting both conformal evaluation and representation-space detectors within a unified streaming architecture. FADES incorporates multiple conformal evaluation variants, including Approximate Cross-Conformal Evaluation, which preserves the statistical structure of cross-conformal evaluation while eliminating repeated model training, as well as an Adaptive Chunking Controller that dynamically balances detection responsiveness and computational cost. We extend prior work through three major contributions: (i) a variance-aware evaluation protocol comprising 375 simulations across multiple seeds and runs, (ii) integration of a contrastive autoencoder-based detector to enable direct comparison between prediction-space and representation-space drift detection, and (iii) expanded evaluation across in-domain and cross-dataset transfer settings using UNSW-NB15, CICIDS2018, and a real-world IoT testbed. Approx-CCE achieves performance comparable to standard cross-conformal evaluation across hundreds of simulations, providing empirical evidence that the statistical benefits of CCE derive primarily from its disjoint calibration partition structure rather than fold-specific model diversity, a finding with implications for conformal evaluation in repeated recalibration settings more broadly. In contrast, representation-space drift detection via CADE incurs substantial computational cost under repeated retraining, limiting its practicality in streaming settings. These findings demonstrate that conformal evaluation provides a statistically grounded and computationally efficient foundation for real-time drift-aware intrusion detection, and that FADES enables flexible, unified evaluation of drift detection strategies under realistic deployment conditions. Full article
(This article belongs to the Special Issue Security and Privacy Challenges in Integrated IoT and Edge Systems)
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51 pages, 1159 KB  
Article
A Secure Cross-Domain Control Mechanism for Stateful Digital Twin Migration in Edge Computing
by Mikail Mohammed Salim, Farheen Naaz and Kwonhue Choi
Electronics 2026, 15(10), 1995; https://doi.org/10.3390/electronics15101995 - 8 May 2026
Viewed by 441
Abstract
Mobility-aware digital twin (DT) migration is increasingly used in edge computing to sustain low-latency service as physical entities and service demand move across domains. However, stateful DT migration across administrative domains requires more than placement adaptation; it also requires target-side legitimacy verification, protected-state [...] Read more.
Mobility-aware digital twin (DT) migration is increasingly used in edge computing to sustain low-latency service as physical entities and service demand move across domains. However, stateful DT migration across administrative domains requires more than placement adaptation; it also requires target-side legitimacy verification, protected-state transfer, continuity-preserving traffic transition, and invalidation of stale source-side instances. This paper presents a secure cross-domain authentication and service continuity mechanism for mobility-aware DT migration in edge computing. The proposed design formulates migration as a six-phase ordered control procedure comprising migration triggering, target-side authorization, protected-state transfer, continuity-aware traffic transition, post-migration activation, and revocation-aware completion. Security analysis examines authorization soundness, migration-state confidentiality and integrity, transition safety, and post-migration uniqueness. Performance evaluation shows that the full mechanism introduces only a bounded increase in migration-related cost while reducing service interruption at 500 MB from approximately 1.79 s without continuity-aware transition control to 285 ms in the full mechanism. The results indicate that the proposed mechanism preserves the operational benefit of mobility-aware DT migration while strengthening migration authorization, state transfer protection, and service continuity under cross-domain relocation. Full article
(This article belongs to the Special Issue Security and Privacy Challenges in Integrated IoT and Edge Systems)
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18 pages, 695 KB  
Article
Certificateless Proxy Re-Encryption Scheme for the Internet of Medical Things
by Han-Yu Lin, Ching-Wei Yeh and Chi-Shiu Chen
Electronics 2025, 14(23), 4654; https://doi.org/10.3390/electronics14234654 - 26 Nov 2025
Viewed by 678
Abstract
With the rapid development of the Internet of Medical Things (IoMT), the data generated and collected by various sensors and medical devices are gradually increasing. How to realize flexible, efficient, and secure data sharing while ensuring data confidentiality and patient privacy has become [...] Read more.
With the rapid development of the Internet of Medical Things (IoMT), the data generated and collected by various sensors and medical devices are gradually increasing. How to realize flexible, efficient, and secure data sharing while ensuring data confidentiality and patient privacy has become a critical research challenge. The traditional Public Key Infrastructure (PKI) must deal with the complicated certificate management problem. An identity-based cryptosystem has the inherent key-escrow risk. These concerns make them unsuitable for resource-constrained and dynamic IoMT environments. To address it, this paper introduces a cloud data sharing protocol for IoMT using a Certificateless Proxy Re-encryption (CL-PRE) scheme that integrates an efficient access-list-based user revocation mechanism. In our system, a patient’s data can be encrypted and securely stored in a semi-trusted third party like the cloud server. When the patient wants to grant the access to designated users, e.g., doctors or medical institutions, a delegated proxy server will re-encrypt the ciphertext to a new one, which is decryptable by the designators. The proxy server also learns nothing during the re-encryption process, so as to maintain the end-to-end confidentiality. As for the security, the authors formally prove that the proposed CL-PRE mechanism for IoMT achieves Type-I and Type-II indistinguishability against adaptive chosen-identity and chosen-ciphertext attacks (IND-PrID-CCA) under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Moreover, the functional and computational comparisons with previous studies reveal the qualitative advantage of simultaneously achieving certificateless properties and user revocation, and the quantitative advantage of an optimized encryption cost (requiring only one bilinear pairing and two scalar multiplications), making it a theoretically efficient solution for resource-constrained IoMT devices. Full article
(This article belongs to the Special Issue Security and Privacy Challenges in Integrated IoT and Edge Systems)
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