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IoT Cybersecurity: 2nd Edition
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IoT Cybersecurity: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 8429

Special Issue Editors

Dr. Himanshu Agrawal

E-Mail Website
Guest Editor
School of Electrical Engineering, Comp and Math Sci (EECMS), Curtin University, Perth, Australia
Interests: networking; Internet of Things; cybersecurity; AI; and machine learning for networking/security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Neminath Hubballi

E-Mail Website
Guest Editor
Discipline of Computer Science and Engineering, Indian Institute of Technology, Indore 453552, India
Interests: network security; system security; software-defined networking and fault detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) cyber security refers to the protection of connected devices and networks. The IoT encompasses the telecommunications network that tethers devices, objects, animals, and people to the Internet. Due to the escalating threat of cyberattacks, cybersecurity has emerged as a crucial domain on the Internet of Things (IoT). Organizations and users are able to mitigate cybersecurity risks by protecting their IoT assets and privacy via IoT cybersecurity. IoT security management can be enhanced via the application of novel cybersecurity technologies and tools. This Special Issue aims to collect current research regarding the application of IoT and cybersecurity technologies.

The potential topics of this Special Issue include, but are not limited to, the following:

  • Security and privacy in IoT;
  • Blockchain-based cybersecurity applications;
  • Emerging security issues and trends in IoT;
  • Privacy, trust, and reliability in IoT;
  • Cybersecurity and data privacy in IoT scenarios;
  • Security information in IoT environment;
  • The role of AI and machine learning in IoT cybersecurity;
  • The role of government and industry in promoting IoT security standards and best practices.

Dr. Himanshu Agrawal
Prof. Dr. Neminath Hubballi
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. Sensors 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 2600 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

  • network security
  • IoT cybersecurity
  • IoT security management

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Related Special Issue

Published Papers (5 papers)

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Research

20 pages, 4163 KB  
Article
Adaptive Multi-Model Hierarchical Federated Learning for Robust IoT Intrusion Detection
by Shahid Latif and Djamel Djenouri
Sensors 2026, 26(10), 3198; https://doi.org/10.3390/s26103198 - 19 May 2026
Viewed by 238
Abstract
The rapid growth of the Internet of Things (IoT) has introduced significant cybersecurity challenges in highly distributed, heterogeneous, and privacy-sensitive environments. Traditional centralized intrusion detection approaches and conventional federated learning (FL) frameworks, which rely on single-model aggregation, are often inadequate in the presence [...] Read more.
The rapid growth of the Internet of Things (IoT) has introduced significant cybersecurity challenges in highly distributed, heterogeneous, and privacy-sensitive environments. Traditional centralized intrusion detection approaches and conventional federated learning (FL) frameworks, which rely on single-model aggregation, are often inadequate in the presence of extreme non-IID data and adversarial conditions. This study proposes an Adaptive Multi-Model Hierarchical Federated Learning (AMM-HFL) framework for robust IoT intrusion detection. The framework operates across client, edge, and cloud tiers and introduces a unified integration of similarity-aware clustering, multi-model aggregation, and dynamic client-side model selection. Unlike existing hierarchical FL approaches, AMM-HFL maintains multiple global models, enabling adaptive personalization and improved representation of heterogeneous data distributions. At the edge level, model updates are clustered to isolate anomalous contributions, while the cloud performs meta-aggregation to refine diverse model representations. Experimental evaluation on the IDSIoT2024 dataset demonstrates detection accuracy up to 96.83–97.54% under IID and 95.64–97.52% under non-IID conditions, while maintaining low computational and cryptographic overhead. Full article
(This article belongs to the Special Issue IoT Cybersecurity: 2nd Edition)
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28 pages, 3895 KB  
Article
Advancing Machine Learning Strategies for Power Consumption-Based IoT Botnet Detection
by Almustapha A. Wakili, Saugat Guni, Sabbir Ahmed Khan, Wei Yu and Woosub Jung
Sensors 2025, 25(24), 7553; https://doi.org/10.3390/s25247553 - 12 Dec 2025
Cited by 1 | Viewed by 1084
Abstract
The proliferation of Internet of Things (IoT) devices has amplified botnet risks, while traditional network-based intrusion detection systems (IDSs) struggle under encrypted and/or sparse traffic. Power consumption offers an effective side channel for device-level detection. Yet, prior studies typically focus on a single [...] Read more.
The proliferation of Internet of Things (IoT) devices has amplified botnet risks, while traditional network-based intrusion detection systems (IDSs) struggle under encrypted and/or sparse traffic. Power consumption offers an effective side channel for device-level detection. Yet, prior studies typically focus on a single model family (often a convolutional neural network (CNN)) and rarely assess generalization across devices or compare broader model classes. In this paper, we conduct unified benchmarking and comparison of classical (SVM and RF), deep (CNN, LSTM, and 1D Transformer), and hybrid (CNN + LSTM, CNN + Transformer, and CNN + RF) models on the CHASE’19 dataset and a newly curated three-class botnet dataset, using consistent preprocessing and evaluation across single- and cross-device settings, reporting both accuracy and efficiency (latency and throughput). Experimental results demonstrate that Random Forest achieves the highest single-device accuracy (99.43% on the Voice Assistant with Seed 42), while CNN + Transformer shows a strong accuracy–efficiency trade-off in cross-device scenarios (94.02% accuracy on the combined dataset at ∼60,000 samples/s when using the best-performing Seed 42). These results offer practical guidance for selecting models under accuracy, latency, and throughput constraints and establish a reproducible baseline for power-side-channel IDSs. Full article
(This article belongs to the Special Issue IoT Cybersecurity: 2nd Edition)
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24 pages, 3395 KB  
Article
ECACS: An Enhanced Certificateless Authentication Scheme for Smart Car Sharing
by Zhuowei Shen, Xiao Kou and Taiyao Yang
Sensors 2025, 25(17), 5441; https://doi.org/10.3390/s25175441 - 2 Sep 2025
Cited by 2 | Viewed by 1151
Abstract
Driven by the demand for cost-effective vehicle access, enhanced flexibility, and sustainable transportation practices, smart car-sharing has emerged as a prominent alternative to traditional vehicle rental systems. Leveraging the Internet of Vehicles (IoV) and wireless communication, these systems feature dynamic renter-vehicle mappings, enabling [...] Read more.
Driven by the demand for cost-effective vehicle access, enhanced flexibility, and sustainable transportation practices, smart car-sharing has emerged as a prominent alternative to traditional vehicle rental systems. Leveraging the Internet of Vehicles (IoV) and wireless communication, these systems feature dynamic renter-vehicle mappings, enabling users to access any available vehicle rather than being restricted to a specific one pre-assigned by the service provider. However, many existing schemes in the IoV field conflate users and vehicles, complicating the identification and tracking of the vehicle’s actual driver. Moreover, most current authentication protocols rely on a strict, initial binding between a user and a vehicle, rendering them unsuitable for the dynamic nature of car-sharing environments. To address these challenges, we propose an enhanced certificateless signature scheme tailored for smart car-sharing. By employing a biometric fuzzy extractor and the Chinese Remainder Theorem, our scheme provides a fine-grained authentication mechanism that eliminates the need for local computations on the user’s side, meaning users do not require a smartphone or other digital device. Furthermore, our scheme introduces category identifiers to facilitate vehicle selection based on specific classes within car-sharing contexts. A formal security analysis demonstrates that our scheme is existentially unforgeable against adversaries under the random oracle model. Finally, a comprehensive evaluation shows that our proposed scheme achieves competitive performance in terms of computational and communication overhead while offering enhanced practical functionalities. Full article
(This article belongs to the Special Issue IoT Cybersecurity: 2nd Edition)
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14 pages, 404 KB  
Article
A New Efficient and Provably Secure Certificateless Signature Scheme Without Bilinear Pairings for the Internet of Things
by Zhanzhen Wei, Xiaoting Liu, Hong Zhao, Zhaobin Li and Bowen Liu
Sensors 2025, 25(17), 5224; https://doi.org/10.3390/s25175224 - 22 Aug 2025
Cited by 3 | Viewed by 1133
Abstract
Pairing-free certificateless signature (PF-CLS) schemes are ideal authentication solutions for resource-constrained environments like the Internet of Things (IoT) due to their low computational, storage, and communication resource requirements. However, it has come to light that many PF-CLS schemes are vulnerable to forged signature [...] Read more.
Pairing-free certificateless signature (PF-CLS) schemes are ideal authentication solutions for resource-constrained environments like the Internet of Things (IoT) due to their low computational, storage, and communication resource requirements. However, it has come to light that many PF-CLS schemes are vulnerable to forged signature attacks. In this paper, we use a novel attack method to prove that a class of PF-CLS schemes with the same security vulnerabilities cannot resist Type I adversary attacks, and we find that, even if some schemes are improved to invalidate existing attack methods, they still cannot defend against the new attack method proposed in this paper. Subsequently, we introduce an enhanced scheme proven to be resilient against both types of adversary attacks under the random oracle model (ROM). Performance analysis shows that, compared with several existing PF-CLS schemes, our scheme offers higher computational efficiency. Full article
(This article belongs to the Special Issue IoT Cybersecurity: 2nd Edition)
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21 pages, 1351 KB  
Article
Enhanced Anomaly Detection in IoT Networks Using Deep Autoencoders with Feature Selection Techniques
by Hamza Rhachi, Younes Balboul and Anas Bouayad
Sensors 2025, 25(10), 3150; https://doi.org/10.3390/s25103150 - 16 May 2025
Cited by 13 | Viewed by 3889
Abstract
An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network [...] Read more.
An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network that interconnected IoT devices uses advanced communications norms and technologies to capture and transmit data. Still, these networks are subject to various types of attacks that will lead to the loss of user data. Concurrently, the field of anomaly detection for the Internet of Things (IoT) is experiencing rapid expansion. This expansion requires a thorough analysis of application trends and existing gaps. Furthermore, it is critical in detecting interesting phenomena such as device damage and unknown events. However, this task is tough due to the unpredictable nature of anomalies and the complexity of the environment. This paper offers a technique that uses an autoencoder neural network to identify anomalous network communications in IoT networks. More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. The proposed model is validated using the NSL-KDD dataset. Compared to some IoT-based anomaly detection models, the experimental results reveal that the suggested model is more efficient at enhancing the accuracy of detecting malicious data. The simulation results show that it works better, with an overall accuracy rate of 85% and 92% successively for the binary and multi-class classifications. Full article
(This article belongs to the Special Issue IoT Cybersecurity: 2nd Edition)
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