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Advanced Technologies for Detecting Cybersecurity Attacks in Internet of Things...
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Advanced Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems

A special issue of Journal of Cybersecurity and Privacy (ISSN 2624-800X). This special issue belongs to the section "Security Engineering & Applications".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5966

Special Issue Editors

Dr. Kamran Siddique

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Alaska Anchorage, Anchorage, AK 99508, USA
Interests: big data processing; cyber security; machine learning; cloud computing; system architectures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ka Lok Man

E-Mail Website
Guest Editor
School of Advanced Technology, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
Interests: AI; IoT; big data analytics; IC designs; WSN
Special Issues, Collections and Topics in MDPI journals
Dr. Zahid Akhtar

E-Mail Website
Guest Editor
Department of Network and Computer Security, State University of New York Polytechnic Institute, C135, Kunsela Hall, Utica, NY 13502, USA
Interests: machine learning and computer vision with applications to cybersecurity; biometrics; deepfakes; affect recognition; image and video processing; perceptual-based audiovisual multimedia quality assessmentsing; perceptual-based audiovisual multimedia quality assessment; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid proliferation of the Internet of Things (IoT) has revolutionized various domains, including healthcare, smart cities, industrial automation, and critical infrastructure. However, the exponential growth of interconnected devices has also significantly increased the attack surface, making IoT systems prime targets for cyber threats. Traditional security mechanisms often fall short in addressing such evolving landscape of cyber threats due to the heterogeneity, resource constraints, and distributed architecture of IoT ecosystems.

This Special Issue aims to explore innovative methodologies, machine learning techniques, and advanced security frameworks designed to enhance IoT security. We invite high-quality contributions that focus on the design, development, and deployment of advanced technologies for the real-time detection, analysis, and mitigation of cybersecurity threats.

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

  • Intelligent systems for intrusion detection in IoT networks;
  • Machine learning and AI-driven security analytics for IoT;
  • Anomaly detection using advanced techniques;
  • Lightweight security frameworks for IoT devices;
  • Blockchain-based security mechanisms for IoT threat detection;
  • Edge and fog computing approaches for real-time threat monitoring;
  • Privacy-preserving techniques for IoT environments;
  • Secure data fusion and sensor networks for cybersecurity;
  • Case studies and real-world implementations of IoT threat detection systems.

By bringing together cutting-edge research, this Special Issue aims to stimulate the development of robust security solutions that ensure the resilience and trustworthiness of IoT ecosystems.

Dr. Kamran Siddique
Prof. Dr. Ka Lok Man
Dr. Zahid Akhtar
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Cybersecurity and Privacy 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 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
  • cybersecurity attacks
  • anomaly detection
  • intrusion detection
  • machine learning
  • AI-driven security
  • blockchain
  • edge computing
  • threat monitoring
  • data fusion
  • privacy-preserving techniques

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

Published Papers (4 papers)

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Research

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31 pages, 1964 KB  
Article
IoT Vulnerability Severity Prediction Using Lightweight Transformer Models
by Samira A. Baho and Jemal Abawajy
J. Cybersecur. Priv. 2026, 6(1), 36; https://doi.org/10.3390/jcp6010036 - 14 Feb 2026
Viewed by 742
Abstract
Vulnerability severity assessment plays a critical role in cybersecurity risk management by quantifying risk based on vulnerability disclosure reports. However, interpreting these reports and assigning reliable risk levels remains challenging in Internet of Things (IoT) environments. This paper proposes an IoT vulnerability severity [...] Read more.
Vulnerability severity assessment plays a critical role in cybersecurity risk management by quantifying risk based on vulnerability disclosure reports. However, interpreting these reports and assigning reliable risk levels remains challenging in Internet of Things (IoT) environments. This paper proposes an IoT vulnerability severity prediction framework aligned with the Common Vulnerability Scoring System (CVSS). The framework is based on a lightweight transformer architecture. It uses a distilled version of Bidirectional Encoder Representations from Transformers (BERT). The model is fine-tuned using transfer learning to capture contextual semantic information from vulnerability descriptions. The lightweight design preserves computational efficiency. Experimental evaluation on an IoT vulnerability dataset shows strong and consistent performance across all severity classes. The proposed model achieves double-digit improvements across key evaluation metrics. In most cases, the improvement exceeds 20% compared with traditional machine learning and baseline deep learning approaches. These results show that lightweight transformer models are well suited for IoT security. They provide a practical and effective solution for automated vulnerability severity classification in resource- and data-constrained environments. Full article
(This article belongs to the Special Issue Advanced Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems)
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16 pages, 822 KB  
Article
Deep Learning Approaches for Multi-Class Classification of Phishing Text Messages
by Miriam L. Munoz and Muhammad F. Islam
J. Cybersecur. Priv. 2025, 5(4), 102; https://doi.org/10.3390/jcp5040102 - 21 Nov 2025
Viewed by 1565
Abstract
Phishing attacks, particularly Smishing (SMS phishing), have become a major cybersecurity threat, with attackers using social engineering tactics to take advantage of human vulnerabilities. Traditional detection models often struggle to keep up with the evolving sophistication of these attacks, especially on devices with [...] Read more.
Phishing attacks, particularly Smishing (SMS phishing), have become a major cybersecurity threat, with attackers using social engineering tactics to take advantage of human vulnerabilities. Traditional detection models often struggle to keep up with the evolving sophistication of these attacks, especially on devices with constrained computational resources. This research proposes a chain transformer model that integrates GPT-2 for synthetic data generation and BERT for embeddings to detect Smishing within a multiclass dataset, including minority smishing variants. By utilizing compact, open-source transformer models designed to balance accuracy and efficiency, this study explores improved detection of phishing threats on text-based platforms. Experimental results demonstrate an accuracy rate exceeding 97% in detecting phishing attacks across multiple categories. The proposed chained transformer model achieved an F1-score of 0.97, precision of 0.98, and recall of 0.96, indicating strong overall performance. Full article
(This article belongs to the Special Issue Advanced Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems)
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Review

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28 pages, 495 KB  
Review
Securing the Cognitive Layer: A Survey on Security Threats, Defenses, and Privacy-Preserving Architectures for LLM-IoT Integration
by Ayan Joshi and Sabur Baidya
J. Cybersecur. Priv. 2026, 6(2), 63; https://doi.org/10.3390/jcp6020063 - 2 Apr 2026
Viewed by 897
Abstract
The convergence of Large Language Models (LLMs) and Internet of Things (IoT) systems has created a new class of intelligent applications across healthcare, industrial automation, smart cities, and connected homes. However, this integration introduces a complex and largely underexplored security landscape. LLMs deployed [...] Read more.
The convergence of Large Language Models (LLMs) and Internet of Things (IoT) systems has created a new class of intelligent applications across healthcare, industrial automation, smart cities, and connected homes. However, this integration introduces a complex and largely underexplored security landscape. LLMs deployed in IoT contexts face threats spanning both the AI and embedded systems domains, including prompt injection through sensor-driven inputs, model extraction from edge devices, data poisoning of IoT data streams, and privacy leakage through LLM-generated responses grounded in personal data. Simultaneously, LLMs are proving to be powerful tools for IoT security, with LLM-based intrusion detection systems achieving 95–99% accuracy on standard IoT datasets and LLM-driven threat intelligence outperforming traditional machine learning by significant margins. We systematically review 88 papers from IEEE, ACM, MDPI, and arXiv (2020–2025), providing: (1) a structured taxonomy of security threats targeting LLM-IoT systems, (2) a review of LLMs as security enablers for IoT, (3) an evaluation of privacy-preserving architectures including federated learning, differential privacy, homomorphic encryption, and trusted execution environments, (4) domain-specific security analysis across healthcare, industrial, smart home, smart grid, and vehicular IoT, and (5) a literature-based comparative analysis of LLM-based security systems. A central finding is the accuracy–efficiency–privacy trilemma: the model compression techniques needed to deploy LLMs on resource-constrained IoT devices can degrade security and even introduce new vulnerabilities. Our analysis provides researchers and practitioners with a structured understanding of both the risks and opportunities at the frontier of LLM-IoT security. Full article
(This article belongs to the Special Issue Advanced Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems)
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38 pages, 1444 KB  
Review
A Comprehensive Review: The Evolving Cat-and-Mouse Game in Network Intrusion Detection Systems Leveraging Machine Learning
by Qutaiba Alasad, Meaad Ahmed, Shahad Alahmed, Omer T. Khattab, Saba Alaa Abdulwahhab and Jiann-Shuin Yuan
J. Cybersecur. Priv. 2026, 6(1), 13; https://doi.org/10.3390/jcp6010013 - 4 Jan 2026
Viewed by 1676
Abstract
Machine learning (ML) techniques have significantly enhanced decision support systems to render them more accurate, efficient, and faster. ML classifiers in securing networks, on the other hand, face a disproportionate risk from the sophisticated adversarial attacks compared to other areas, such as spam [...] Read more.
Machine learning (ML) techniques have significantly enhanced decision support systems to render them more accurate, efficient, and faster. ML classifiers in securing networks, on the other hand, face a disproportionate risk from the sophisticated adversarial attacks compared to other areas, such as spam filtering, intrusion, and virus detection, and this introduces a continuous competition between malicious users and preventers. Attackers test ML models with inputs that have been specifically crafted to evade these models and obtain inaccurate forecasts. This paper presents a comprehensive review of attack and defensive techniques in ML-based NIDSs. It highlights the current serious challenges that the systems face in preserving robustness against adversarial attacks. Based on our analysis, with respect to their current superior performance and robustness, ML-based NIDS require urgent attention to develop more robust techniques to withstand such attacks. Finally, we discuss the current existing approaches in generating adversarial attacks and reveal the limitations of current defensive approaches. In this paper, the most recent advancements, such as hybrid defensive techniques that integrate multiple strategies to prevent adversarial attacks in NIDS, have highlighted the ongoing challenges. Full article
(This article belongs to the Special Issue Advanced Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems)
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