Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Security and Privacy in IoT Devices and Computing
share announcement

Security and Privacy in IoT Devices and Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 2744

Special Issue Editors

Dr. Saikat Das

E-Mail Website
Guest Editor
Computer Science, Utah Valley University, Orem, UT 84058, USA
Interests: cyber security; Internet of Things; machine learning; interpretable ML; intrusion detection system; moving target defense
Dr. Sajal Saha

E-Mail Website
Guest Editor
Department of Computer Science, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada
Interests: machine learning for intelligent wireless communications; Internet of Things/everything (IoT/IoE) security; cyber-attack detection; internet traffic analysis
Dr. Qudrat E Alahy Ratul

E-Mail Website
Guest Editor
Computer Science, Utah Valley University, Orem, UT 84058, USA
Interests: machine learning; artificial intelligence; data science; interpretable machine learning; natural language processing; generative model; LLM; adversarial attack

Special Issue Information

Dear Colleagues,

This Special Issue explores the multifaceted security and privacy challenges associated with the Internet of Things (IoT), emphasizing innovative solutions grounded in machine learning, blockchain, and quantum computing. It also examines the role of low-power, low-bandwidth protocols (such as Bluetooth Low Energy (BLE), ZigBee, and Z-Wave) and machine-to-machine communication protocols (such as MQTT and CoAP) in addressing these challenges. The aim of this Special Issue is to present practical and cutting-edge strategies for enhancing IoT security and privacy through a series of case studies, surveys, and original research articles. By focusing on these areas, this Special issue aims to foster collaboration among researchers, practitioners, and stakeholders to tackle the pressing security and privacy issues present in IoT environments.

Focus

The primary focus of this Special Issue is the real-world implications of cyber-attacks on IoT systems and the development of robust solutions to mitigate these risks. Topics include, but are not limited to, device identification, access control, intrusion detection, malware analysis, and software exploitation. This Special Issue also delves into advanced concepts such as lightweight cryptography, quantum-era security solutions, and trust management in IoT.

Scope

The scope of this Special Issue covers a broad range of topics crucial to IoT security and privacy:

  • Device fingerprinting and machine learning-based device identification
  • Federated learning for enhanced privacy and security
  • Blockchain applications for securing IoT systems
  • Intrusion detection mechanisms and malware analysis techniques
  • Firmware security and third-party firmware evaluation
  • Security considerations for narrowband IoT networks
  • Methods for detecting zero-day vulnerabilities
  • Security measures for communication protocols like BLE, ZigBee, and Z-Wave
  • Privacy-enhancing techniques for MQTT and CoAP
  • Quantum and post-quantum security solutions
  • Security frameworks for IoT-based healthcare and agricultural systems
  • Software-defined IoT network security
  • Trust management and experimental testbeds for IoT environments
  • Government and industry roles in ensuring IoT security
  • Case studies highlighting successful and secure IoT deployments

By addressing these diverse topics, this Special Issue not only contributes to the existing literature but also provides actionable insights and solutions to enhance the security and privacy of IoT systems, paving the way for safer and more reliable IoT deployments.

Relationship to Existing Literature

This Special Issue aims to supplement the existing literature by providing comprehensive insights into emerging security and privacy challenges in the IoT landscape. While previous studies have addressed specific aspects of IoT security, this collection seeks to integrate these perspectives into a cohesive framework that addresses both current and future challenges. It builds on established research while introducing novel approaches and technologies, such as federated learning and blockchain, to advance the field. By doing so, it fills gaps in the existing body of knowledge and offers a holistic view of IoT security and privacy, promoting a deeper understanding of this critical area and fostering innovation.

Dr. Saikat Das
Dr. Sajal Saha
Dr. Qudrat E Alahy Ratul
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. Electronics 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 2400 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

  • Internet of Things
  • security and privacy
  • machine learning
  • intrusion detection
  • malware analysis
  • block chain
  • quantum computing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

36 pages, 6507 KiB  
Article
Strategic Network Attack Prevention System Leveraging Sophisticated Query-Based Network Attention Algorithm (QNAA) and Self-Perpetuating Generative Adversarial Network (SPF-GAN) Techniques for Optimal Detection
by Tahani Albalawi, Perumal Ganeshkumar and Faisal Albalwy
Electronics 2025, 14(5), 922; https://doi.org/10.3390/electronics14050922 - 26 Feb 2025
Viewed by 477
Abstract
Network attack detection is a critical issue in complex networks at present, one which becomes even more challenging as the network complexity grows and new threats emerge. Existing security models may encounter problems such as low accuracy, a high number of false positives, [...] Read more.
Network attack detection is a critical issue in complex networks at present, one which becomes even more challenging as the network complexity grows and new threats emerge. Existing security models may encounter problems such as low accuracy, a high number of false positives, and the inability to learn new attacks, especially jamming attacks, where the attacker floods a communication channel with noise. Hence, an adaptive and resilient approach is required. This study presents two novel approaches—the Query-Based Network Attention Algorithm (QNAA) and the Self-Perpetuating Generative Adversarial Network (SPF-GAN) —to enhance performance and flexibility. The QNAA integrates attention mechanisms that allow the model to focus on features and patterns connected with attacks, while the SPF-GAN applies generative adversarial networks to mimic attack scenarios, improving the model’s predictive capability and robustness. The assessment outcomes indicate that the formulated model yields a higher accuracy, precision, recall, and F1-scores than conventional methods in identifying jammer attacks on different datasets. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Devices and Computing)
►▼ Show Figures

Figure 1

13 pages, 265 KiB  
Article
Efficient Elliptic Curve Diffie–Hellman Key Exchange for Resource-Constrained IoT Devices
by Vinayak Tanksale
Electronics 2024, 13(18), 3631; https://doi.org/10.3390/electronics13183631 - 12 Sep 2024
Cited by 4 | Viewed by 1861
Abstract
In the era of ubiquitous connectivity facilitated by the Internet of Things (IoT), ensuring robust security mechanisms for communication channels among resource-constrained devices has become imperative. Elliptic curve Diffie–Hellman (ECDH) key exchange offers strong security assurances and computational efficiency. This paper investigates the [...] Read more.
In the era of ubiquitous connectivity facilitated by the Internet of Things (IoT), ensuring robust security mechanisms for communication channels among resource-constrained devices has become imperative. Elliptic curve Diffie–Hellman (ECDH) key exchange offers strong security assurances and computational efficiency. This paper investigates the challenges and opportunities of deploying ECDH key exchange protocols on resource-constrained IoT devices. We review the fundamentals of ECDH and explore optimization techniques tailored to the limitations of embedded systems, including memory constraints, processing power, and energy efficiency. We optimize the implementation of five elliptic curves and compare them using experimental results. Our experiments focus on electronic control units and sensors in vehicular networks. The findings provide valuable insights for IoT developers, researchers, and industry stakeholders striving to enhance the security posture of embedded IoT systems while maintaining efficiency. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Devices and Computing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop