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Electronics
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Computer Networking Security and Privacy
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Computer Networking Security and Privacy

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

Deadline for manuscript submissions: 15 August 2026 | Viewed by 4053

Special Issue Editors

Dr. Baihe Ma

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: differential privacy; data privacy; IOV privacy; cybersecurity; machine learning privacy
Dr. Mst Shapna Akter

E-Mail Website
Guest Editor
Department of Computer Science, Oakland University, Rochester, MI 48309-447, USA
Interests: natural language processing (NLP); machine learning (ML); deep learning (DL) applications; health security; AI security; quantum technology to identify software vulnerabilities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of information technology has positioned computer networks as an indispensable component of societal infrastructure. However, this technological progression has concurrently precipitated escalating security vulnerabilities and privacy infringements that manifest as sophisticated cyber threats. As a result, effective methods to safeguard network security and privacy have become a crucial research focus in both academia and industry.

This Special Issue seeks to curate pioneering advancements in computer network security and privacy. Aligned with the interdisciplinary focus of Electronics, this initiative prioritizes research at the nexus of secure network architecture resilience, adaptive cyber-physical system governance, and machine learning-driven threat intelligence frameworks. This Special Issue endeavors to establish a transformative knowledge repository that catalyzes paradigm shifts in cybersecurity strategy formulation, ethical AI deployment, and next-generation privacy-preserving computational models.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

(1) Privacy protection;

(2) Cybersecurity;

(3) Federated learning;

(4) Split learning;

(5) Unlearning mechanisms;

(6) Differential privacy;

(7) Blockchain;

(8) Distributed learning.

Dr. Baihe Ma
Dr. Mst Shapna Akter
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-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

  • privacy
  • AI privacy
  • cybersecurity
  • distributed learning
  • unlearning mechanisms

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Published Papers (5 papers)

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Research

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37 pages, 499 KB  
Article
Comparative Analysis of Attribute-Based Encryption Schemes for Special Internet of Things Applications
by Łukasz Pióro, Krzysztof Kanciak and Zbigniew Zieliński
Electronics 2026, 15(3), 697; https://doi.org/10.3390/electronics15030697 - 5 Feb 2026
Abstract
Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, [...] Read more.
Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, and context-aware security management in modern Internet of Things (IoT) applications. ABAC controls access based on attributes associated with users, devices, resources, and environmental conditions rather than fixed roles, making it highly adaptable to the complex and heterogeneous nature of IoT ecosystems. ABE can significantly improve the security and manageability of modern military IoT systems. Nevertheless, its practical implementation requires obtaining a range of performance data and assessing the additional overhead, particularly regarding data transmission efficiency. This paper provides a comparative analysis of the performance of two cryptographic schemes for attribute-based encryption in the context of special Internet of Things (IoT) applications. This applies to special environments, both military and civilian, where infrastructure is unreliable and dynamic and decisions must be made locally and in near-real time. From a security perspective, there is a need for strong authentication, precise access control, and a zero-trust approach at the network edge as well. The CIRCL scheme, based on traditional pairing-based ABE (CP-ABE), is compared with the newer Covercrypt scheme, a hybrid key encapsulation mechanism with access control (KEMAC) that provides quantum resistance. The main goal is to determine which scheme scales better and meets the performance requirements for two different scenarios: large corporate networks (where scalability is key) and tactical edge networks (where minimal bandwidth and post-quantum security are paramount). The benchmark results are used to compare the operating costs in detail, such as the key generation time, message encryption and decryption times, public key size, and cipher overhead, showing that Covercrypt provides a reduction in ciphertext overhead in tactical scenarios, while CIRCL offers faster decryption throughput in large-scale enterprise environments. It is concluded that the optimal choice depends on the specific constraints of the operating environment. Full article
(This article belongs to the Special Issue Computer Networking Security and Privacy)
32 pages, 2215 KB  
Article
AuditableLLM: A Hash-Chain-Backed, Compliance-Aware Auditable Framework for Large Language Models
by Delong Li, Guangsheng Yu, Xu Wang and Bin Liang
Electronics 2026, 15(1), 56; https://doi.org/10.3390/electronics15010056 - 23 Dec 2025
Viewed by 1081
Abstract
Auditability and regulatory compliance are increasingly required for deploying large language models (LLMs). Prior work typically targets isolated stages such as training or unlearning and lacks a unified mechanism for verifiable accountability across model updates. This paper presents AuditableLLM, a lightweight framework that [...] Read more.
Auditability and regulatory compliance are increasingly required for deploying large language models (LLMs). Prior work typically targets isolated stages such as training or unlearning and lacks a unified mechanism for verifiable accountability across model updates. This paper presents AuditableLLM, a lightweight framework that decouples update execution from an audit-and-verification layer and records each update as a hash-chain-backed, tamper-evident audit trail. The framework supports parameter-efficient fine-tuning such as Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA), full-parameter optimization, continual learning, and data unlearning, enabling third-party verification without access to model internals or raw logs. Experiments on LLaMA-family models with LoRA adapters and the MovieLens dataset show negligible utility degradation (below 0.2% in accuracy and macro-F1) with modest overhead (3.4 ms/step; 5.7% slowdown) and sub-second audit validation in the evaluated setting. Under a simple loss-based membership inference attack on the forget set, the audit layer does not increase membership leakage relative to the underlying unlearning algorithm. Overall, the results indicate that hash-chain-backed audit logging can be integrated into practical LLM adaptation, update, and unlearning workflows with low overhead and verifiable integrity. Full article
(This article belongs to the Special Issue Computer Networking Security and Privacy)
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34 pages, 3725 KB  
Article
ATAW-TM: An Adaptive, Threshold-Free, and Automatically Weighted Trust Model for Mitigating Multiple Types of Denial-of-Service Attacks in Software-Defined Wireless Sensor Networks
by Lijuan Wang, Mee Loong Yang and Krassie Petrova
Electronics 2025, 14(24), 4933; https://doi.org/10.3390/electronics14244933 - 16 Dec 2025
Viewed by 374
Abstract
Wireless sensor networks (WSNs), including Software-Defined Wireless Sensors, are particularly vulnerable to Denial-of-Service (DoS) attacks. Trust models are widely acknowledged as an effective strategy to mitigate the threat of successful DoS attacks in WSNs. However, existing trust models commonly rely on threshold configurations [...] Read more.
Wireless sensor networks (WSNs), including Software-Defined Wireless Sensors, are particularly vulnerable to Denial-of-Service (DoS) attacks. Trust models are widely acknowledged as an effective strategy to mitigate the threat of successful DoS attacks in WSNs. However, existing trust models commonly rely on threshold configurations that are based on the network administrator’s experience and leave the challenging task of weight allocation for various trust metrics to network users. This limits the widespread application of trust models as a WSN defence mechanism. To address that issue, this study proposes and theoretically analyses an Adaptive, Threshold-Free, and Automatically Weighted Trust Model (ATAW-TM) for SDWSNs. The model architecture is aligned with the layered centralized management architecture of SDWSNs, which makes it flexible and enhances its responsiveness. The proposed model does not require manual threshold configuration and weight allocation and allows for rapid trust system recovery. It has significant advantages compared to existing trust models and is potentially more feasible to implement on a large scale. Full article
(This article belongs to the Special Issue Computer Networking Security and Privacy)
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20 pages, 3944 KB  
Article
Performance Analysis and Security Preservation of DSRC in V2X Networks
by Muhammad Saad Sohail, Giancarlo Portomauro, Giovanni Battista Gaggero, Fabio Patrone and Mario Marchese
Electronics 2025, 14(19), 3786; https://doi.org/10.3390/electronics14193786 - 24 Sep 2025
Cited by 1 | Viewed by 1590
Abstract
Protecting communications within vehicular networks is of paramount importance, particularly when data are transmitted using wireless ad-hoc technologies such as Dedicated Short-Range Communications (DSRC). Vulnerabilities in Vehicle-to-Everything (V2X) communications, especially along highways, pose significant risks, such as unauthorized interception or alteration of vehicle [...] Read more.
Protecting communications within vehicular networks is of paramount importance, particularly when data are transmitted using wireless ad-hoc technologies such as Dedicated Short-Range Communications (DSRC). Vulnerabilities in Vehicle-to-Everything (V2X) communications, especially along highways, pose significant risks, such as unauthorized interception or alteration of vehicle data. This study proposes a Software-Defined Radio (SDR)-based tool designed to assess the protection level of V2X communication systems against cyber attacks. The proposed tool can emulate both reception and transmission of IEEE 802.11p packets while testing DSRC implementation and robustness. The results of this investigation offer valuable contributions toward shaping cybersecurity strategies and frameworks designed to protect the integrity of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. Full article
(This article belongs to the Special Issue Computer Networking Security and Privacy)
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Review

Jump to: Research

21 pages, 321 KB  
Review
Privacy-Preserving Protocols in Smart Cities and Industrial IoT: Challenges, Trends, and Future Directions
by Manuel José Cabral dos Santos Reis
Electronics 2026, 15(2), 399; https://doi.org/10.3390/electronics15020399 - 16 Jan 2026
Viewed by 374
Abstract
The increasing deployment of interconnected devices in Smart Cities and Industrial Internet of Things (IIoT) environments has significantly enhanced operational efficiency, automation, and real-time data analytics. However, this rapid digitization also introduces complex security and privacy challenges, particularly in the handling of sensitive [...] Read more.
The increasing deployment of interconnected devices in Smart Cities and Industrial Internet of Things (IIoT) environments has significantly enhanced operational efficiency, automation, and real-time data analytics. However, this rapid digitization also introduces complex security and privacy challenges, particularly in the handling of sensitive data across heterogeneous and resource-constrained networks. This review explores the current landscape of privacy-preserving protocols designed for Smart City and IIoT infrastructures. We examine state-of-the-art approaches including lightweight cryptographic schemes, secure data aggregation, anonymous communication protocols, and blockchain-based frameworks. The paper also analyzes practical trade-offs between security, latency, and computational overhead in real-world deployments. Open research challenges such as secure interoperability, privacy in federated learning, and resilience against AI-driven cyberattacks are discussed. Finally, the paper outlines promising research directions and technologies that can enable scalable, secure, and privacy-aware network infrastructures for future urban and industrial ecosystems. Full article
(This article belongs to the Special Issue Computer Networking Security and Privacy)
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