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Cybersecurity and Privacy Protection: The Key to IoT Sensors
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Cybersecurity and Privacy Protection: The Key to IoT Sensors

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 654

Special Issue Editors

Dr. Farhan Ullah

E-Mail Website
Guest Editor
School of Software, Northwestern Polytechnical University, Xian 710072, China
Interests: malware analysis; program analysis; data science; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Zhao

E-Mail Website
Guest Editor
School of Computer Science, College of Science, Mathematics and Technology at Wenzhou-Kean University, Wenzhou, China
Interests: malware analysis; big data analytics; bioinformatics; medical informatics

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has changed organizations by enabling networked devices to collect and share data automatically. IoT sensors, which are the foundation of the technology, are essential for monitoring and regulating tangible environments. The necessity for robust cybersecurity and privacy protection has become increasingly critical in light of the increasing prevalence of IoT. These sensors are commonly subject to cyber-attacks and privacy violations, which can disclose sensitive data and threaten entire systems.

This special issue focuses on the most current advancements in cybersecurity and privacy solutions for IoT devices. It invites researchers to propose cutting-edge solutions to challenges such as secure data delivery, intrusion detection, encryption, and privacy-preserving strategies. Contributions focus on lightweight cryptography, privacy-enhancing technologies, secure protocols for resource-constrained devices, and novel intrusion detection systems for IoT networks.

As IoT evolves, it becomes increasingly important to provide a secure and confidential environment for these sensors. This special issue intends to encourage academic and industrial collaboration to improve the security and privacy of IoT sensor networks, resulting in a safer and more trustworthy future.

We invite papers that include, but not exclusively, the following topics:

  1. Malware detection and propagation
  2. Intrusion detection
  3. Mobile banking security
  4. Edge and fog computing for IoT
  5. Blockchain security for IoT
  6. Future trends in IoT security
  7. Generative AI for IoT security
  8. Privacy-preserving algorithms for IoT security
  9. Adversarial attacks on IoT
  10. Advanced machine learning for IoT security
  11. Federated Learning for IoT security
  12. Large Language Model (LLM) for IoT

Dr. Farhan Ullah
Dr. Yue Zhao
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

  • IoT security
  • privacy
  • sensor networks

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Published Papers (1 paper)

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Research

29 pages, 1935 KiB  
Article
Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments
by Fiza Ashfaq, Muhammad Wasim, Mumtaz Ali Shah, Abdul Ahad and Ivan Miguel Pires
Sensors 2025, 25(6), 1905; https://doi.org/10.3390/s25061905 - 19 Mar 2025
Viewed by 513
Abstract
The Internet has been vulnerable to several attacks as it has expanded, including spoofing, viruses, malicious code attacks, and Distributed Denial of Service (DDoS). The three main types of attacks most frequently reported in the current period are viruses, DoS attacks, and DDoS [...] Read more.
The Internet has been vulnerable to several attacks as it has expanded, including spoofing, viruses, malicious code attacks, and Distributed Denial of Service (DDoS). The three main types of attacks most frequently reported in the current period are viruses, DoS attacks, and DDoS attacks. Advanced DDoS and DoS attacks are too complex for traditional security solutions, such as intrusion detection systems and firewalls, to detect. The combination of machine learning methods with AI-based machine learning has led to the introduction of several novel attack detection systems. Due to their remarkable performance, machine learning models, in particular, have been essential in identifying DDoS attacks. However, there is a considerable gap in the work on real-time detection of such attacks. This study uses Mininet with the POX Controller to simulate an environment to detect DDoS attacks in real-time settings. The CICDDoS2019 dataset identifies and classifies such attacks in the simulated environment. In addition, a virtual software-defined network (SDN) is used to collect network information from the surrounding area. When an attack occurs, the pre-trained models are used to analyze the traffic and predict the attack in real-time. The performance of the proposed methodology is evaluated based on two metrics: accuracy and detection time. The results reveal that the proposed model achieves an accuracy of 99% within 1 s of the detection time. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy Protection: The Key to IoT Sensors)
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