Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.5
3.6
Journals
Technologies
Special Issues
Research on Security and Privacy of Data and Networks
share announcement

Research on Security and Privacy of Data and Networks

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 6321

Special Issue Editors

Dr. Fazlullah Khan

E-Mail Website
Guest Editor
School of Computer Science, University of Nottingham Ningbo China, Ningbo, China
Interests: security; privacy; machine learning; Internet of Things
Dr. Sikha Bagui

E-Mail
Guest Editor
Department of Computer Science, University of West Florida, Pensacola, FL 32514, USA
Interests: database design; big data architecture; big data analytics; machine learning and data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Ateeq Ur Rehman

E-Mail Website
Guest Editor
Department of Computing, Staffordshire University, Stoke-on-Trent, UK
Interests: next-generation wireless communications; cognitive radio networks; privacy and blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing adoption of intelligent networks, including 5G and IoT, and next-generation communication systems has brought new opportunities for innovation across various industries. However, with the proliferation of connected devices and data-intensive applications, security and privacy concerns have become critical challenges. This Special Issue of Technologies, entitled "Research on Security and Privacy of Data and Networks", aims to address these concerns by exploring novel approaches, frameworks, and technologies to safeguard data and ensure privacy in intelligent network environments. The focus will be on securing the integrity, confidentiality, and availability of data while enabling privacy-preserving mechanisms that comply with global regulations. This publication will feature research on advanced encryption techniques, secure data transmission protocols, intrusion detection systems, and privacy-enhancing technologies. It will also explore the role of artificial intelligence and machine learning in detecting and mitigating security threats in intelligent networks. As intelligent networks grow, understanding the vulnerabilities and risks associated with data sharing, storage, and transmission becomes increasingly important for ensuring the protection of sensitive information.

Dr. Fazlullah Khan
Dr. Sikha Bagui
Dr. Ateeq Ur Rehman
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. Technologies is an international peer-reviewed open access monthly 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 1800 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

  • intelligent networks
  • data security
  • privacy protection
  • machine learning in security
  • intrusion detection

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 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

Jump to: Other

41 pages, 6103 KB  
Article
H-RT-IDPS: A Hierarchical Real-Time Intrusion Detection and Prevention System for the Smart Internet of Vehicles via TinyML-Distilled CNN and Hybrid BiLSTM-XGBoost Models
by Ikram Hamdaoui, Chaymae Rami, Zakaria El Allali and Khalid El Makkaoui
Technologies 2025, 13(12), 572; https://doi.org/10.3390/technologies13120572 - 5 Dec 2025
Viewed by 995
Abstract
The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system [...] Read more.
The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article
(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)
►▼ Show Figures

Figure 1

28 pages, 4127 KB  
Article
Deep Residual Learning for Face Anti-Spoofing: A Mathematical Framework for Optimized Skip Connections
by Ardak Nurpeisova, Anargul Shaushenova, Oleksandr Kuznetsov, Aidar Ispussinov, Zhazira Mutalova and Akmaral Kassymova
Technologies 2025, 13(9), 413; https://doi.org/10.3390/technologies13090413 - 11 Sep 2025
Viewed by 1714
Abstract
Face anti-spoofing is crucial for protecting biometric authentication systems. Presentation attacks using 3D masks and high-resolution printed images present detection challenges for existing methods. In this paper, we introduce a family of specialized CNN architectures, AttackNet, designed for robust face anti-spoofing with optimized [...] Read more.
Face anti-spoofing is crucial for protecting biometric authentication systems. Presentation attacks using 3D masks and high-resolution printed images present detection challenges for existing methods. In this paper, we introduce a family of specialized CNN architectures, AttackNet, designed for robust face anti-spoofing with optimized residual connections and activation functions. The study includes the development of four architectures: baseline LivenessNet, AttackNetV1 with concatenation-based skip connections, AttackNetV2.1 with optimized activation functions, and AttackNetV2.2 with efficient addition-based residual learning. Our analysis demonstrates that element-wise addition in skip connections reduces parameters from 8.4 M to 4.2 M while maintaining performance. A comprehensive evaluation was conducted on four benchmark datasets: MSSpoof, 3DMAD, CSMAD, and Replay-Attack. Results show high accuracy (approaching 100%) on the 3DMAD, CSMAD, and Replay-Attack datasets. On the more challenging MSSpoof dataset, AttackNetV1 achieved 99.6% accuracy with an HTER of 0.004, outperforming the baseline LivenessNet (94.35% accuracy, 0.056 HTER). Comparative analysis with state-of-the-art methods confirms the superiority of the proposed approach. AttackNetV2.2 demonstrates an optimal balance between accuracy and computational efficiency, requiring 16.1 MB of memory compared to 32.1 MB for other AttackNet variants. Training time analysis shows twice the speed for AttackNetV2.2 compared to AttackNetV1. Architectural ablation studies highlight the crucial role of residual connections, batch normalization, and suitable dropout rates. Statistical significance testing verifies the reliability of the results (p-value < 0.001). The proposed architectures show excellent generalization ability and practical usefulness for real-world deployment in mobile and embedded systems. Full article
(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)
►▼ Show Figures

Figure 1

21 pages, 1658 KB  
Article
Emotionally Controllable Text Steganography Based on Large Language Model and Named Entity
by Hao Shi, Wenpu Guo and Shaoyuan Gao
Technologies 2025, 13(7), 264; https://doi.org/10.3390/technologies13070264 - 21 Jun 2025
Viewed by 2624
Abstract
For the process of covert transmission of text information, in addition to the need to ensure the quality of the text at the same time, it is also necessary to make the text content match the current context. However, the existing text steganography [...] Read more.
For the process of covert transmission of text information, in addition to the need to ensure the quality of the text at the same time, it is also necessary to make the text content match the current context. However, the existing text steganography methods excessively pursue the quality of the text, and lack constraints on the content and emotional expression of the generated steganographic text (stegotext). In order to solve this problem, this paper proposes an emotionally controllable text steganography based on large language model and named entity. The large language model is used for text generation to improve the quality of the generated stegotext. The named entity recognition is used to construct an entity extraction module to obtain the current context-centered text and constrain the text generation content. The sentiment analysis method is used to mine the sentiment tendency so that the stegotext contains rich sentiment information and improves its concealment. Through experimental validation on the generic domain movie reviews dataset IMDB, the results prove that the proposed method has significantly improved hiding capacity, perplexity, and security compared with the existing mainstream methods, and the stegotext has a strong connection with the current context. Full article
(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

34 pages, 1728 KB  
Systematic Review
Integrating Machine Learning and Business Intelligence into Supply Chain Risk Management for a Comprehensive Cybersecurity Framework: A Systematic Literature Review
by Rasha Aljaafreh, Firas Al-Doghman, Farookh Hussain, Fazlullah Khan and Ali Aljaafreh
Technologies 2026, 14(4), 194; https://doi.org/10.3390/technologies14040194 - 24 Mar 2026
Viewed by 197
Abstract
Supply chain cybersecurity is a growing concern for businesses as they utilize increasingly interconnected digital systems. This systematic literature review examines how machine learning (ML) and business intelligence (BI) may be used in conjunctions to improve supply chain cyber security risk management. This [...] Read more.
Supply chain cybersecurity is a growing concern for businesses as they utilize increasingly interconnected digital systems. This systematic literature review examines how machine learning (ML) and business intelligence (BI) may be used in conjunctions to improve supply chain cyber security risk management. This review followed PRISMA guidelines. A quality evaluation was performed based on CASP to evaluate 35 peer-reviewed articles published in 2016–2025. The review analysis indicates that although ML has been extensively utilized for threat detection, BI utilization is fragmented. Additionally, there is a lack of integrated ML-BI frameworks, specifically for small–medium enterprises (SMEs) and developing economies. As such, this literature review provides a conceptual four-layer framework of predictive and analytical capabilities for threat detection, risk assessment, and decision-making. It also identifies a structured research agenda with which to advance the field of research. Full article
(This article belongs to the Special Issue Research on Security and Privacy of Data and 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 1-4
Back to TopTop