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Novel Methods Applied to Security and Privacy Problems in Future...
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Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies, Volume II

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

Deadline for manuscript submissions: 15 December 2026 | Viewed by 9595

Editors

Prof. Dr. Irfan Awan

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: network security; cyber security; performance modeling of cloud and communication networks
Special Issues, Collections and Topics in MDPI journals
Dr. Amna Qureshi

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: designing, analysing; implementing cryptographic protocols with security and privacy guarantees using concepts of applied cryptography; distributed systems; game theory; logic programming
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Shahwaiz Afaqui

E-Mail Website
Guest Editor
Electric & Electronic Engineering, SCEDT Engineering, Teesside University, Middlesbrough TS1 3BX, UK
Interests: WLANs and WPANs (frequency, energy, interference management, among others); cross-layer optimization; network security; 3GPP LTE-WLAN aggregation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Future networking technologies refer to emerging and developing technologies that are anticipated to shape the ways in which we connect, communicate, and share data in the future. These technologies have the potential to revolutionise the way we interact with the world, enabling rapid and more efficient communication. Examples of these technologies include 5G, 6G, blockchain, IoT, cloud computing, and Software-Defined Networking (SDN), among others. Although these technologies offer seamless communication and facilitate the advancement of new applications and services that have not been conceivable with the current networking technologies, these generate a number of security and privacy challenges. Some of the critical challenges to overcome include an increased attack surface, new attack vectors, evolving threats, data privacy, and user trust. These are only a few of the security and privacy challenges that must be addressed in order to build a secure and trustworthy future for networking technologies.

To cope with the aforementioned challenges, this Special Issue welcomes original and innovative perspectives on theories, methodologies, schemes, algorithms, and systems related to all aspects of security and privacy in future networking technologies from academia, industry, and government. We invite the contribution of original research papers, survey papers, and position papers to this Special Issue. Potential topics include, but are not limited to, the following:

  • End-to-end communication security, privacy, and trust;
  • Security and privacy protection in 5G and beyond;
  • Trust, security, and privacy in cloud/edge computing;
  • Lightweight and privacy-preserving authentication mechanisms;
  • Lightweight identify and access management mechanisms;
  • Quantum cryptography;
  • Intrusion detection and prevention systems for network security;
  • Privacy preservation in AI-enabled networks;
  • Zero trust techniques, architectures, and models;
  • Security and privacy challenges in internet of things (IOT) networks;
  • Security, safety, and reliability in industrial internet of things (IIOT);
  • Secure and privacy-preserving techniques for blockchain in 5g/6g;
  • Sensing security and privacy for IEEE 802.11.

Prof. Dr. Irfan Awan
Dr. Amna Qureshi
Dr. Muhammad Shahwaiz Afaqui
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-anonymized 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

  • security
  • privacy
  • trust
  • 5G/6G
  • AI
  • blockchain
  • IoT

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Related Special Issue

Published Papers (4 papers)

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Research

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23 pages, 1996 KB  
Article
Trustworthy Visual Privacy Auditing with Causal Governance and Resilient Federated Protection for NIST AI Risk Management Framework
by Ray-I Chang, Wei-Xun Lu and Chih Yang
Electronics 2026, 15(8), 1658; https://doi.org/10.3390/electronics15081658 - 15 Apr 2026
Viewed by 387
Abstract
Our previous visual privacy framework leveraging Graph Convolutional Networks (GCNs) and Federated Learning (FL) has been shown to achieve state-of-the-art (SOTA) predictive performance. However, it neglects the systemic requirements of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI [...] Read more.
Our previous visual privacy framework leveraging Graph Convolutional Networks (GCNs) and Federated Learning (FL) has been shown to achieve state-of-the-art (SOTA) predictive performance. However, it neglects the systemic requirements of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF). To address this critical gap, this paper proposes the Trustworthy Visual Privacy Auditing (TVPA) system, which transitions conventional static detection models into a dynamic and secure governance ecosystem. We first establish system resilience against adversarial threats by proposing an active auditing mechanism called Resilient Federated Protection (RFP) to embed unique model parameter watermarks within client-side updates. The RFP mechanism enables the federated aggregator to verify node legitimacy and automatically isolate malicious clients attempting poisoning attacks. Then, to ensure strict accountability, we design an immutable audit log mechanism in the RFP mechanism that utilizes a Cryptographic Hash Chain (CHC) to record and verify the provenance of every model update, creating a transparent chain of custody. Furthermore, the prediction mechanism is enhanced by Causal Governance (CG) that integrates causal inference to provide counterfactual reasoning for explaining the root causes of privacy risks rather than merely flagging associations. Experiments on the VISPR dataset demonstrate that our TVPA system can synthesize high-performance recognition with robust security, auditability, and causal explainability to provide trustworthy AI governance. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies, Volume II)
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33 pages, 2850 KB  
Article
Automated Vulnerability Scanning and Prioritisation for Domestic IoT Devices/Smart Homes: A Theoretical Framework
by Diego Fernando Rivas Bustos, Jairo A. Gutierrez and Sandra J. Rueda
Electronics 2026, 15(2), 466; https://doi.org/10.3390/electronics15020466 - 21 Jan 2026
Viewed by 1397
Abstract
The expansion of Internet of Things (IoT) devices in domestic smart homes has created new conveniences but also significant security risks. Insecure firmware, weak authentication and weak encryption leave households exposed to privacy breaches, data leakage and systemic attacks. Although research has addressed [...] Read more.
The expansion of Internet of Things (IoT) devices in domestic smart homes has created new conveniences but also significant security risks. Insecure firmware, weak authentication and weak encryption leave households exposed to privacy breaches, data leakage and systemic attacks. Although research has addressed several challenges, contributions remain fragmented and difficult for non-technical users to apply. This work addresses the following research question: How can a theoretical framework be developed to enable automated vulnerability scanning and prioritisation for non-technical users in domestic IoT environments? A Systematic Literature Review of 40 peer-reviewed studies, conducted under PRISMA 2020 guidelines, identified four structural gaps: dispersed vulnerability knowledge, fragmented scanning approaches, over-reliance on technical severity in prioritisation and weak protocol standardisation. The paper introduces a four-module framework: a Vulnerability Knowledge Base, an Automated Scanning Engine, a Context-Aware Prioritisation Module and a Standardisation and Interoperability Layer. The framework advances knowledge by integrating previously siloed approaches into a layered and iterative artefact tailored to households. While limited to conceptual evaluation, the framework establishes a foundation for future work in prototype development, household usability studies and empirical validation. By addressing fragmented evidence with a coherent and adaptive design, the study contributes to both academic understanding and practical resilience, offering a pathway toward more secure and trustworthy domestic IoT ecosystems. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies, Volume II)
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28 pages, 4730 KB  
Article
Machine Learning for Cybersecurity: A Survey of Applications, Adversarial Challenges, and Future Research Directions
by Zefeng He, Diego Davila, Shengping Bi, Tao Wang and Tao Hou
Electronics 2025, 14(23), 4563; https://doi.org/10.3390/electronics14234563 - 21 Nov 2025
Cited by 3 | Viewed by 6802
Abstract
The convergence of ubiquitous connectivity, large-scale data generation, and rapid advancements in machine learning is transforming the field of cybersecurity. The widespread adoption of interconnected systems including Internet of Things devices, mobile platforms, and cloud infrastructures has introduced new attack surfaces and significantly [...] Read more.
The convergence of ubiquitous connectivity, large-scale data generation, and rapid advancements in machine learning is transforming the field of cybersecurity. The widespread adoption of interconnected systems including Internet of Things devices, mobile platforms, and cloud infrastructures has introduced new attack surfaces and significantly increased the complexity of securing digital environments. Concurrently, these technologies have enabled the development of intelligent, data-driven defense strategies. Achieving effective protection in these settings requires not only applying machine learning to detect and prevent threats but also recognizing that such models can themselves become targets of adversarial manipulation. This survey presents a comprehensive analysis of recent progress at the intersection of machine learning and cybersecurity. It explores defensive applications such as malware detection, network traffic classification, and anomaly detection, as well as offensive strategies including adversarial evasion, poisoning, and backdoor attacks. Particular attention is paid to adversarial machine learning, highlighting the increasing sophistication of attacks that exploit model vulnerabilities and the corresponding evolution of defense mechanisms. Beyond synthesizing current research, the survey also identifies key open challenges and emerging research directions. This survey provides a comprehensive and accessible reference for researchers and practitioners aiming to understand and advance the secure application of machine learning across diverse cybersecurity domains. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies, Volume II)
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Review

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39 pages, 3309 KB  
Review
Security in Collaborative Driving: A Survey of Threats, Defenses, and Emerging Trends
by Sahil Nayak, Onat Gungor and Tajana Rosing
Electronics 2026, 15(11), 2389; https://doi.org/10.3390/electronics15112389 - 1 Jun 2026
Viewed by 314
Abstract
Collaborative driving, in which autonomous vehicles cooperate with other vehicles and roadside infrastructure to improve safety, perception, and traffic efficiency, is emerging as a key paradigm for next-generation transportation systems. While such collaboration enhances situational awareness, it also introduces new security vulnerabilities across [...] Read more.
Collaborative driving, in which autonomous vehicles cooperate with other vehicles and roadside infrastructure to improve safety, perception, and traffic efficiency, is emerging as a key paradigm for next-generation transportation systems. While such collaboration enhances situational awareness, it also introduces new security vulnerabilities across perception, communication, planning, decision-making, and control layers. In this survey, we present a unified taxonomy of security threats and defense mechanisms in collaborative driving systems, systematically organizing attacks and countermeasures across system layers. We further examine the integration of language models, including vision-based and multimodal reasoning models, into collaborative driving pipelines, highlighting the resulting security risks and design challenges. Finally, we identify key open research challenges, including cross-layer and end-to-end security, uncertainty-aware defenses, and real-world validation, outlining promising directions for future work toward secure and resilient collaborative autonomous mobility. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies, Volume II)
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