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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: 20 July 2025 | Viewed by 512

Special Issue Editors

Dr. Himanshu Agrawal

E-Mail Website
Guest Editor
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6100, 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 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. 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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Published Papers (1 paper)

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Research

21 pages, 1351 KiB  
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
Viewed by 257
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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