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Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends
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Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends

A Special Issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 3496

Editors

Dr. Bin Hu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA
Interests: mobile sensing and computing; cybersecurity and privacy; efficient deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiacun Wang

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USA
Interests: machine learning; software engineering; discrete event systems; formal methods; wireless networking; real-time distributed systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xiwang Guo

E-Mail Website
Guest Editor
1. Information and Control Engineering College, Liaoning Petrochemical University, Fushun 113001, China
2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: deep reinforcement learning; intelligent optimization algorithms; autonomous vehicles; detection model; artificial intelligence; intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid proliferation of Internet of Things (IoT) devices has significantly transformed modern industries, smart cities, healthcare, and everyday life. However, this widespread adoption has also introduced critical security and privacy concerns. As IoT networks continue to expand, new threats and vulnerabilities emerge, requiring novel and efficient cybersecurity strategies. Addressing these challenges is essential to ensure the integrity, confidentiality, and availability of IoT ecosystems.

This Special Issue on "Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends" aims to explore state-of-the-art security mechanisms, risk mitigation techniques, and emerging trends in safeguarding IoT devices and networks. We welcome original research and review articles that contribute to advancing security models, cryptographic techniques, privacy-preserving frameworks, and trust mechanisms tailored for IoT environments.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • IoT Security Architectures and Threat Models;
  • Lightweight Cryptography and Secure Communication for IoT;
  • Privacy-Preserving Mechanisms in IoT Applications;
  • AI-Driven Cybersecurity for IoT Devices;
  • Blockchain-Based Security Solutions for IoT;
  • Intrusion Detection and Prevention in IoT Networks;
  • Secure Firmware Updates and IoT Device Management;
  • Edge Computing and IoT Security Challenges;
  • Cybersecurity for Industrial IoT (IIoT) and Smart Cities;
  • Trust Management and Access Control in IoT Systems;
  • Machine Learning for IoT Security.

You may choose our Joint Special Issue in Electronics.

Dr. Bin Hu
Prof. Dr. Jiacun Wang
Dr. Xiwang Guo
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. Network is an international peer-reviewed open access quarterly 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 1200 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
  • lightweight cryptography
  • AI in cybersecurity
  • privacy-preserving IoT
  • blockchain for IoT
  • intrusion detection
  • edge computing security
  • trust management
  • smart city cybersecurity

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

29 pages, 6477 KB  
Article
A Risk-Gated Security Attention Mechanism for Rare-Threat Detection in Industrial IoT Networks
by Shaimaa Ahmed Elsaid, Ahmed M. Saad and Eslam Mahmoud Fouda
Network 2026, 6(3), 72; https://doi.org/10.3390/network6030072 - 8 Sep 2026
Viewed by 182
Abstract
In current state-of-the-art intrusion detection systems (IDSs), models are trained to detect the repetitive behavior of data traffic. This leads to the problem of ignoring rare yet important attacks. To solve the problem stated above, a risk-gated security attention (RGSA) architecture is proposed. [...] Read more.
In current state-of-the-art intrusion detection systems (IDSs), models are trained to detect the repetitive behavior of data traffic. This leads to the problem of ignoring rare yet important attacks. To solve the problem stated above, a risk-gated security attention (RGSA) architecture is proposed. The initialization of the risk estimator based on the CVE/CVSS severity score enables the attention network to detect important yet rare patterns. Furthermore, a tier-aware focal loss is proposed to mitigate security threats in the real world without any data augmentation process. Evaluations were conducted on held-out 80/20 test splits of the CIC-IDS2017, CIC-IDS2018, and CIC-IoT2023 datasets. The calibrated binary detection process reached false negative rates (FNRs) of 0.50% and 0.40% on the CIC-IDS2017 and CIC-IDS2018 datasets, respectively, remaining below the 1.0% FNR operational target adopted in this study. For micro-support critical threats, precise discrete analysis had to be applied to maintain statistical validity. Also, weighted F1-scores of at least 99.65% were consistently achieved for all testbeds analyzed. This network contains 284,317 trainable parameters and strictly avoids the creation of any synthetic samples through complete rejection of synthetic oversampling. The findings indicate that adding priors for security tiers to the attention process is favorable for moving towards an impact-based threat management approach. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends)
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17 pages, 340 KB  
Article
Efficient Serial Systolic Polynomial Multiplier for Lattice-Based Post-Quantum Cryptographic Schemes in IoT Edge Node
by Atef Ibrahim and Fayez Gebali
Network 2026, 6(2), 21; https://doi.org/10.3390/network6020021 - 1 Apr 2026
Viewed by 747
Abstract
The rapid development of the Internet of Things (IoT) is transforming various economic and industrial sectors by embedding interconnected devices within their operational processes. However, security and privacy risks associated with these interconnected devices pose significant barriers to widespread adoption, particularly in light [...] Read more.
The rapid development of the Internet of Things (IoT) is transforming various economic and industrial sectors by embedding interconnected devices within their operational processes. However, security and privacy risks associated with these interconnected devices pose significant barriers to widespread adoption, particularly in light of potential quantum threats. To mitigate these challenges, it is imperative to employ post-quantum cryptographic schemes. However, essential constraints on IoT edge nodes complicate the effective implementation of such schemes. Among the most promising approaches in post-quantum cryptography are lattice-based schemes, which rely heavily on polynomial multiplication operations at their core. Improving the implementation of polynomial multiplication will significantly enhance the performance of these schemes. Therefore, this paper proposes an efficent low-complexity serial systolic array optimized for polynomial multiplication, particularly tailored for the Binary Ring Learning With Errors (BRLWE) scheme. Designed for cryptographic processors targeting capable IoT edge nodes, the proposed architecture demonstrates remarkable performance improvements, achieving a maximum operating frequency of 280 MHz for a field size of 256, while requiring only 8232 lookup tables (LUTs) and 2616 flip-flops (FFs). These results reflect a 16.8% reduction in LUT usage and a 19% reduction in FFs compared to the nearest competing designs, all while maintaining high throughput and low area utilization. This work significantly advances the establishment of secure and efficient infrastructure for IoT systems, bolstering their resilience against post-quantum attacks and supporting the growth of a robust digital economy. Furthermore, it aligns with sustainable development goals 8 and 9 by fostering trust and facilitating the adoption of cutting-edge IoT technologies, ultimately promoting more resilient and innovative economic activities. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends)
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30 pages, 6746 KB  
Article
Securing IoT Networks Using Machine Learning-Resistant Physical Unclonable Functions (PUFs) on Edge Devices
by Abdul Manan Sheikh, Md. Rafiqul Islam, Mohamed Hadi Habaebi, Suriza Ahmad Zabidi, Athaur Rahman bin Najeeb and Mazhar Baloch
Network 2026, 6(1), 6; https://doi.org/10.3390/network6010006 - 12 Jan 2026
Cited by 4 | Viewed by 1814
Abstract
The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device [...] Read more.
The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device level due to limited computing resources, mobility, and the large diversity of devices, as well as at the network level, where the use of varied protocols by different vendors introduces further vulnerabilities. Physical Unclonable Functions (PUFs) provide a lightweight, hardware-based security primitive that exploits inherent device-specific variations to ensure uniqueness, unpredictability, and enhanced protection of data and user privacy. Additionally, modeling attacks against PUF architectures is challenging due to the random and unpredictable physical variations inherent in their design, making it nearly impossible for attackers to accurately replicate their unique responses. This study collected approximately 80,000 Challenge Response Pairs (CRPs) from a Ring Oscillator (RO) PUF design to evaluate its resilience against modeling attacks. The predictive performance of five machine learning algorithms, i.e., Support Vector Machines, Logistic Regression, Artificial Neural Networks with a Multilayer Perceptron, K-Nearest Neighbors, and Gradient Boosting, was analyzed, and the results showed an average accuracy of approximately 60%, demonstrating the strong resistance of the RO PUF to these attacks. The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. The p-values from the 15 statistical tests confirm that the CRP data exhibit true randomness, with most values exceeding the 0.01 threshold and supporting the null hypothesis of randomness. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends)
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