Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Security and Privacy-Preserving Techniques for IoT and Communication Networks
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Security and Privacy-Preserving Techniques for IoT and Communication Networks

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 3088

Special Issue Editors

Dr. Aftab Ali

E-Mail Website
Guest Editor
School of Computing, Ulster University, 2-24 York Street, Belfast BT15 1AP, UK
Interests: cyber security; applied AI; IoT security and privacy; key agreement; body area networks; blockchains
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christopher Nugent

E-Mail Website
Guest Editor
School of Computing, Ulster University, 2-24 York Street, Belfast BT15 1AP, UK
Interests: ambient assisted living; smart homes; smart environments; activity recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Farrukh Aslam Khan

E-Mail Website
Guest Editor
Center of Excellence in Information Assurance, King Saud University, Riyadh 11653, Saudi Arabia
Interests: cyber security; smart grid; energy consumption forecasting; ad hoc and sensor networks; internet of things; e-health; computational intelligence; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and convenience, but it has also raised significant concerns regarding security and privacy. This Special Issue aims to explore innovative approaches and solutions for enhancing security and preserving privacy in IoT and communication networks. It seeks to bring together researchers and practitioners to address the challenges posed by the increasing complexity and scale of interconnected devices, while also considering the diverse application domains of IoT technologies.

Topics for the Special Issue:

  1. Secure communication protocols;
  2. Privacy-preserving data management;
  3. Intrusion Detection and Prevention Systems (IDPS) in IoT;
  4. Blockchain for IoT security;
  5. Edge computing security;
  6. Vulnerability exploitation in the IoT;
  7. Lightweight cryptography in the IoT;
  8. Trust management in IoT;
  9. Physical-layer security for IoT;
  10. Privacy-enhancing technologies (PETs) tailored for IoT;
  11. Future trends in IoT security and privacy;
  12. Regulatory and ethical considerations for IoT;
  13. Digital-twin-based threat modelling for IoT;
  14. IoT security monitoring with digital twins.

Dr. Aftab Ali
Prof. Dr. Christopher Nugent
Prof. Dr. Farrukh Aslam Khan
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
  • edge computing
  • cyber security

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

23 pages, 785 KiB  
Article
Efficient IoT User Authentication Protocol with Semi-Trusted Servers
by Shunfang Hu, Yuanyuan Zhang, Yanru Guo, Wang Zhong, Yanru Chen and Liangyin Chen
Sensors 2025, 25(7), 2013; https://doi.org/10.3390/s25072013 - 23 Mar 2025
Viewed by 221
Abstract
Internet of Things (IoT) user authentication protocols enable secure authentication and session key negotiation between users and IoT devices via an intermediate server, allowing users to access sensor data or control devices remotely. However, the existing IoT user authentication schemes often assume that [...] Read more.
Internet of Things (IoT) user authentication protocols enable secure authentication and session key negotiation between users and IoT devices via an intermediate server, allowing users to access sensor data or control devices remotely. However, the existing IoT user authentication schemes often assume that the servers (registration center and intermediate servers) are fully trusted, overlooking the potential risk of insider attackers. Moreover, most of the existing schemes lack critical security properties, such as resistance to ephemeral secret leakage attacks and offline password guessing attacks, and they are unable to provide perfect forward security. Furthermore, with the rapid growth regarding IoT devices, the servers must manage a large number of users and device connections, making the performance of the authentication scheme heavily reliant on the server’s computational capacity, thereby impacting the system’s scalability and efficiency. The design of security protocols is based on the underlying security model, and the current IoT user authentication models fail to cover crucial threats like insider attacks and ephemeral secret leakage. To overcome these limitations, we propose a new security model, IoT-3eCK, which assumes semi-trusted servers and strengthens the adversary model to better meet the IoT authentication requirements. Based on this model, we design an efficient protocol that ensures user passwords, biometric data, and long-term keys are protected from insider users during registration, mitigating insider attacks. The protocol also integrates dynamic pseudo-identity anonymous authentication and ECC key exchange to satisfy the security properties. The performance analysis shows that, compared to the existing schemes, the new protocol reduces the communication costs by over 23% and the computational overhead by more than 22%, with a particularly significant reduction of over 95% in the computational overhead at the intermediate server. Furthermore, the security of the protocol is rigorously demonstrated using the random oracle model and verified with automated tools, further confirming its security and reliability. Full article
(This article belongs to the Special Issue Security and Privacy-Preserving Techniques for IoT and Communication Networks)
►▼ Show Figures

Figure 1

25 pages, 1972 KiB  
Article
FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning
by Rabia Khan, Noshina Tariq, Muhammad Ashraf, Farrukh Aslam Khan, Saira Shafi and Aftab Ali
Sensors 2024, 24(17), 5834; https://doi.org/10.3390/s24175834 - 8 Sep 2024
Viewed by 2124
Abstract
The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and [...] Read more.
The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy. Full article
(This article belongs to the Special Issue Security and Privacy-Preserving Techniques for IoT and Communication Networks)
►▼ 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