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IoT
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Cybersecurity in the Age of the Internet of Things
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Cybersecurity in the Age of the Internet of Things

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 993

Special Issue Editor

Dr. Firoz Khan

E-Mail Website
Guest Editor
Center of Information and Communication Sciences, Ball State University, Muncie, IN, USA
Interests: security; IoT; AI; networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid proliferation of Internet of Things (IoT) devices has revolutionized various sectors, including healthcare, smart cities, industrial automation, and critical infrastructure. However, this unprecedented connectivity has also introduced significant cybersecurity risks, rendering IoT ecosystems a prime target for cyberattacks. Threats such as unauthorized access, denial-of-service (DoS) attacks, malware propagation, and data breaches pose serious concerns for privacy, reliability, and safety. Traditional security models often fall short in addressing the unique challenges of the IoT, given its resource-constrained devices, heterogeneous architectures, and dynamic network environments.

This Special Issue, “Cybersecurity in the Age of the Internet of Things”, seeks to advance research on novel security mechanisms, architectures, and frameworks that enhance the resilience of IoT systems. We welcome contributions covering a broad range of topics, including AI-driven threat detection, blockchain-based authentication, privacy-preserving cryptographic techniques, and secure communication protocols. Additionally, we welcome research that explores security challenges in emerging IoT paradigms such as 5G-enabled IoT, edge computing, and the industrial IoT (IIoT). Papers that provide real-world case studies, attack analyses, risk assessment models, and regulatory considerations for IoT security are highly encouraged. By fostering interdisciplinary discussions, this Special Issue aims to bridge the gap between academia, industry, and policymakers, offering insights into next-generation cybersecurity strategies for safeguarding IoT ecosystems.

Dr. Firoz Khan
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. IoT 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 1400 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
  • cyber threat intelligence
  • AI and machine learning in cybersecurity
  • blockchain for IoT security
  • privacy-preserving IoT solutions
  • secure IoT architectures
  • intrusion detection systems for IoT
  • 5G and IoT security
  • edge computing security
  • regulatory and policy challenges in IoT security

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Published Papers (2 papers)

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20 pages, 835 KB  
Article
Trustworthy Adaptive AI for Real-Time Intrusion Detection in Industrial IoT Security
by Mohammad Al Rawajbeh, Amala Jayanthi Maria Soosai, Lakshmana Kumar Ramasamy and Firoz Khan
IoT 2025, 6(3), 53; https://doi.org/10.3390/iot6030053 - 8 Sep 2025
Abstract
Traditional security methods fail to match the speed of evolving threats because Industrial Internet of Things (IIoT) technologies have become more widely adopted. A lightweight adaptive AI-based intrusion detection system (IDS) for IIoT environments is presented in this paper. The proposed system detects [...] Read more.
Traditional security methods fail to match the speed of evolving threats because Industrial Internet of Things (IIoT) technologies have become more widely adopted. A lightweight adaptive AI-based intrusion detection system (IDS) for IIoT environments is presented in this paper. The proposed system detects cyber threats in real time through an ensemble of online learning models that also adapt to changing network behavior. The system implements SHAP (SHapley Additive exPlanations) for model prediction explanations to allow human operators to verify and understand alert causes while addressing the essential need for trust and transparency. The system validation was performed using the ToN_IoT and Bot-IoT benchmark datasets. The proposed system detects threats with 96.4% accuracy while producing 2.1% false positives and requiring 35 ms on average for detection on edge devices with limited resources. Security analysts can understand model decisions through SHAP analysis because packet size and protocol type and device activity patterns strongly affect model predictions. The system underwent testing on a Raspberry Pi 5-based IIoT testbed to evaluate its deployability in real-world scenarios through emulation of practical edge environments with constrained computational resources. The research unites real-time adaptability with explainability and low-latency performance in an IDS framework specifically designed for industrial IoT security. The solution provides a scalable method to boost cyber resilience in manufacturing, together with energy and critical infrastructure sectors. By enabling fast, interpretable, and low-latency intrusion detection directly on edge devices, this solution enhances cyber resilience in critical sectors such as manufacturing, energy, and infrastructure, where timely and trustworthy threat responses are essential to maintaining operational continuity and safety. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of the Internet of Things)
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25 pages, 3109 KB  
Article
Radio Frequency Fingerprinting Authentication for IoT Networks Using Siamese Networks
by Raju Dhakal, Laxima Niure Kandel and Prashant Shekhar
IoT 2025, 6(3), 47; https://doi.org/10.3390/iot6030047 - 22 Aug 2025
Viewed by 609
Abstract
As IoT (internet of things) devices grow in prominence, safeguarding them from cyberattacks is becoming a pressing challenge. To bootstrap IoT security, device identification or authentication is crucial for establishing trusted connections among devices without prior trust. In this regard, radio frequency fingerprinting [...] Read more.
As IoT (internet of things) devices grow in prominence, safeguarding them from cyberattacks is becoming a pressing challenge. To bootstrap IoT security, device identification or authentication is crucial for establishing trusted connections among devices without prior trust. In this regard, radio frequency fingerprinting (RFF) is gaining attention because it is more efficient and requires fewer computational resources compared to resource-intensive cryptographic methods, such as digital signatures. RFF works by identifying unique manufacturing defects in the radio circuitry of IoT devices by analyzing over-the-air signals that embed these imperfections, allowing for the identification of the transmitting hardware. Recent studies on RFF often leverage advanced classification models, including classical machine learning techniques such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), as well as modern deep learning architectures like Convolutional Neural Network (CNN). In particular, CNNs are well-suited as they use multidimensional mapping to detect and extract reliable fingerprints during the learning process. However, a significant limitation of these approaches is that they require large datasets and necessitate retraining when new devices not included in the initial training set are added. This retraining can cause service interruptions and is costly, especially in large-scale IoT networks. In this paper, we propose a novel solution to this problem: RFF using Siamese networks, which eliminates the need for retraining and allows for seamless authentication in IoT deployments. The proposed Siamese network is trained using in-phase and quadrature (I/Q) samples from 10 different Software-Defined Radios (SDRs). Additionally, we present a new algorithm, the Similarity-Based Embedding Classification (SBEC) for RFF. We present experimental results that demonstrate that the Siamese network effectively distinguishes between malicious and trusted devices with a remarkable 98% identification accuracy. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of the Internet of Things)
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