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Cybersecurity in the Age of AI and IoT: Challenges and...
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Cybersecurity in the Age of AI and IoT: Challenges and Innovations

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: closed (10 August 2025) | Viewed by 6017

Special Issue Editors

Dr. Moutaz Alazab

E-Mail Website
Guest Editor
Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan
Interests: cybersecurity; malware analysis; data and network security artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Ammar Alazab

E-Mail Website
Guest Editor
School of IT, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Interests: information security; database; machine learning; smart 5G networks; green 5G communication; sleep mode; Markov decision process; energy efficient 5G networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's rapidly evolving technological landscape, the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and cybersecurity has become increasingly critical. These technologies are transforming various sectors, including healthcare, financial markets, government, industrial automation, smart cities, and local organizations, enabling seamless data collection, analysis, and decision-making. However, this interconnectedness also introduces sophisticated cybersecurity threats that pose significant risks to data integrity, privacy, and system reliability.

This Special Issue will gather pioneering research, innovative solutions, and practical insights from experts in the fields of AI, IoT security, and cybersecurity. We invite authors to submit their original research papers, review articles, case studies, and experimental studies that address, but do not only cover, the following topics:

  • Cybersecurity in AI-driven applications;
  • Threat modeling and risk assessment in IoT systems;
  • Secure communication protocols for AI and IoT systems;
  • Intrusion detection and response mechanisms in IoT networks;
  • Machine learning and AI approaches for enhancing cybersecurity;
  • Privacy and data protection in AI and IoT applications;
  • Secure key management and authentication schemes for IoT;
  • Blockchain technology for securing IoT and AI applications;
  • Cybersecurity challenges in financial markets;
  • Security strategies for government and local organizations;
  • Emerging trends and future directions in AI, IoT, and cybersecurity.

Dr. Moutaz Alazab
Dr. Ammar Alazab
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 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

  • artificial intelligence (AI)
  • internet of things (IoT)
  • cybersecurity
  • AI-driven applications
  • threat modeling
  • risk assessment
  • intrusion detection
  • machine learning
  • privacy protection
  • data protection
  • financial markets security
  • government security strategies
  • emerging trends
  • secure AI applications
  • secure iot systems

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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

25 pages, 1777 KB  
Article
TwinGuard: Privacy-Preserving Digital Twins for Adaptive Email Threat Detection
by Taiwo Oladipupo Ayodele
J. Cybersecur. Priv. 2025, 5(4), 91; https://doi.org/10.3390/jcp5040091 - 29 Oct 2025
Viewed by 688
Abstract
Email continues to serve as a primary vector for cyber-attacks, with phishing, spoofing, and polymorphic malware evolving rapidly to evade traditional defences. Conventional email security systems, often reliant on static, signature-based detection struggle to identify zero-day exploits and protect user privacy in increasingly [...] Read more.
Email continues to serve as a primary vector for cyber-attacks, with phishing, spoofing, and polymorphic malware evolving rapidly to evade traditional defences. Conventional email security systems, often reliant on static, signature-based detection struggle to identify zero-day exploits and protect user privacy in increasingly data-driven environments. This paper introduces TwinGuard, a privacy-preserving framework that leverages digital twin technology to enable adaptive, personalised email threat detection. TwinGuard constructs dynamic behavioural models tailored to individual email ecosystems, facilitating proactive threat simulation and anomaly detection without accessing raw message content. The system integrates a BERT–LSTM hybrid for semantic and temporal profiling, alongside federated learning, secure multi-party computation (SMPC), and differential privacy to enable collaborative intelligence while preserving confidentiality. Empirical evaluations were conducted using both synthetic AI-generated email datasets and real-world datasets sourced from Hugging Face and Kaggle. TwinGuard achieved 98% accuracy, 97% precision, and a false positive rate of 3%, outperforming conventional detection methods. The framework offers a scalable, regulation-compliant solution that balances security efficacy with strong privacy protection in modern email ecosystems. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI and IoT: Challenges and Innovations)
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22 pages, 1339 KB  
Article
AI-Powered Security for IoT Ecosystems: A Hybrid Deep Learning Approach to Anomaly Detection
by Deepak Kumar, Priyanka Pramod Pawar, Santosh Reddy Addula, Mohan Kumar Meesala, Oludotun Oni, Qasim Naveed Cheema, Anwar Ul Haq and Guna Sekhar Sajja
J. Cybersecur. Priv. 2025, 5(4), 90; https://doi.org/10.3390/jcp5040090 - 27 Oct 2025
Cited by 2 | Viewed by 897
Abstract
The rapid expansion of the Internet of Things (IoT) has introduced new vulnerabilities that traditional security mechanisms often fail to address effectively. Signature-based intrusion detection systems cannot adapt to zero-day attacks, while rule-based solutions lack scalability for the diverse and high-volume traffic in [...] Read more.
The rapid expansion of the Internet of Things (IoT) has introduced new vulnerabilities that traditional security mechanisms often fail to address effectively. Signature-based intrusion detection systems cannot adapt to zero-day attacks, while rule-based solutions lack scalability for the diverse and high-volume traffic in IoT environments. To strengthen the security framework for IoT, this paper proposes a deep learning-based anomaly detection approach that integrates Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs). The model is further optimized using the Moth–Flame Optimization (MFO) algorithm for automated hyperparameter tuning. To mitigate class imbalance in benchmark datasets, we employ Generative Adversarial Networks (GANs) for synthetic sample generation alongside Z-score normalization. The proposed CNN–BiGRU + MFO framework is evaluated on two widely used datasets, UNSW-NB15 and UCI SECOM. Experimental results demonstrate superior performance compared to several baseline deep learning models, achieving improvements across accuracy, precision, recall, F1-score, and ROC–AUC. These findings highlight the potential of combining hybrid deep learning architectures with evolutionary optimization for effective and generalizable intrusion detection in IoT systems. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI and IoT: Challenges and Innovations)
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30 pages, 5036 KB  
Article
Chaotic Hénon–Logistic Map Integration: A Powerful Approach for Safeguarding Digital Images
by Abeer Al-Hyari, Mua’ad Abu-Faraj, Charlie Obimbo and Moutaz Alazab
J. Cybersecur. Priv. 2025, 5(1), 8; https://doi.org/10.3390/jcp5010008 - 18 Feb 2025
Cited by 2 | Viewed by 3208
Abstract
This paper presents an integrated chaos-based algorithm for image encryption that combines the chaotic Hénon map and chaotic logistic map (CLM) to enhance the security of digital image communication. The proposed method leverages chaos theory to generate cryptographic keys, utilizing a 1D key [...] Read more.
This paper presents an integrated chaos-based algorithm for image encryption that combines the chaotic Hénon map and chaotic logistic map (CLM) to enhance the security of digital image communication. The proposed method leverages chaos theory to generate cryptographic keys, utilizing a 1D key from the logistic map generator and a 2D key from the chaotic Hénon map generator. These chaotic maps produce highly unpredictable and complex keys essential for robust encryption. Extensive experiments demonstrate the algorithm’s resilience against various attacks, including chosen-plaintext, noise, clipping, occlusion, and known-plaintext attacks. Performance evaluation in terms of encryption time, throughput, and image quality metrics validates the effectiveness of the proposed integrated approach. The results indicate that the chaotic Hénon–logistic map integration provides a powerful and secure method for safeguarding digital images during transmission and storage with a key space that reaches up to 2200. Moreover, the algorithm has potential applications in secure image sharing, cloud storage, and digital forensics, inspiring new possibilities. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI and IoT: Challenges and Innovations)
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