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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 11811

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Editors

Dr. Fazlullah Khan

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Guest Editor

School of Computer Science, University of Nottingham Ningbo China, Ningbo, China
Interests: security; privacy; machine learning; Internet of Things

Dr. Sikha Bagui

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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
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Dr. Ateeq Ur Rehman

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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-anonymized 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

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Published Papers (8 papers)

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Research

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29 pages, 10432 KB

Open AccessArticle

A Physical-Layer Threat Detection Framework for Secure IoT and Smart Grid Networks Using HHT-Based Multimodal Deep Learning

by Jie Ren, Chunhai Zhou, Chuyang Tan and Yan Wang

Technologies 2026, 14(7), 423; https://doi.org/10.3390/technologies14070423 - 11 Jul 2026

Viewed by 250

Abstract

Secure IoT and smart grid networks depend on reliable hardware operation to maintain continuous service and system availability. Physical-layer abnormalities such as partial discharge (PD) can weaken infrastructure components and disrupt connected systems before conventional monitoring methods detect the problem. PD is one of the earliest indicators of abnormal hardware activity in electrical infrastructure. If it is not detected in time, it can damage equipment, reduce system reliability, and increase the risk of service interruption in intelligent network environments. Existing detection methods often struggle with PD signals because these signals are non-stationary, vary over time, and frequently contain noise. This limits reliable physical-layer threat detection in secure IoT and smart grid networks. This study presents an integrated physical-layer threat-detection framework for secure IoT and smart grid networks that combines adaptive HHT-based signal decomposition with multimodal deep learning for early hardware threat identification. The framework first applies the Hilbert–Huang Transform (HHT) to decompose PD signals and extract time–frequency features that describe discharge behavior. A convolutional neural network with an attention-based fusion mechanism then learns patterns from electrical and acoustic signals. The model classifies hardware condition into normal operation, early abnormal activity, and critical discharge states associated with potential hardware threats. The framework is evaluated using two public datasets: the Dataset of Partial Discharge and Noise Signals and the Partial Discharge Localization (PD-Loc) dataset available through the IEEE DataPort. Experimental evaluation shows that the proposed framework achieves 97.8% detection accuracy, a 97.0% F1-score, and an average AUC of 0.98. The framework maintains 94.6% accuracy under severe noise conditions (10 dB SNR) and performs inference in approximately 12 ms per sample. Furthermore, component-wise analysis further shows that HHT-based feature extraction improves detection accuracy from 91.8% to 95.6%, while multimodal learning increases the final accuracy to 97.8%. Full article

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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29 pages, 3277 KB

Open AccessArticle

MiniLM-CNN-LSTM: A Lightweight Hybrid Transformer Model for Malicious URL Detection

by Emad-ul-Haq Qazi, Muhammad Hamza Faheem and Abdulrazaq Almorjan

Technologies 2026, 14(6), 316; https://doi.org/10.3390/technologies14060316 - 24 May 2026

Viewed by 817

Abstract

Phishing and malicious websites are a serious threat on the internet. Attackers use fake links to trick users and steal their private information. Detecting these links is difficult because attackers change their tricks often. Many old methods cannot detect new or hidden threats. Some recent models use deep learning (DL), but they are large, slow, and hard to use in real-time systems. In this paper, we present a lightweight and accurate model called MiniLM-CNNLSTM. It combines a small transformer model (MiniLM) with a hybrid DL network using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers. The transformer learns the meaning of URLs. The CNN finds important patterns. The LSTM captures the order of characters. We also add handcrafted features that help the model detect tricky URLs. We test our method on two public datasets: the Phishing Site URLs dataset and the Malicious URLs dataset from Kaggle. We use 3-fold cross-validation and early stopping to ensure fair and stable results. The MiniLM-CNN-LSTM model outperformed previous benchmarks by achieving an average three-fold cross-validation accuracy of 98.98%, a precision of 98.63%, a recall of 98.29%, an F1-score of 98.46%, and a false positive rate of 0.68%. The proposed model has a higher accuracy, precision, recall, F1-score and a lower false positive rate, which enhances the accuracy by 1.88, precision by 3.77, recall by 4.17 and decreases the false positive rate by 61.58% compared with the strongest baseline (Distil BERT + CNN-LSTM), showing significant practical improvements. The results show that our approach is fast, small, and highly effective. It can detect phishing and malicious links with high accuracy. This makes it a good choice for real-time security systems like browsers, email filters, or firewalls. Full article

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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29 pages, 4028 KB

Open AccessArticle

Selecting a Cybersecurity Risk Analysis Methodology for MSMEs Using a Multi-Criteria Method (AHP)

by Gabriel Enrique Taborda Blandon, Juan Fernando Hurtado Rivera, Javier Mauricio Durán Vásquez, Maria José Monsalve Ruiz, Marco Tulio Silva Castillo and Hector Fernando Vargas Montoya

Technologies 2026, 14(4), 227; https://doi.org/10.3390/technologies14040227 - 14 Apr 2026

Cited by 1 | Viewed by 952

Abstract

In the current context of digital transformation, Micro-, Small-, and Medium-Sized Enterprises (MSMEs) are increasingly exposed to cybersecurity risks. This exposure is intensified by the limited adoption of international standards for identifying impacts, low budgets, and shortages of trained personnel, which collectively result in the absence of structured control plans for mitigating cyber risks. (1) This study proposes a mechanism for selecting a cybersecurity risk analysis and management methodology suited to Colombian MSMEs by applying the multi-criteria Analytic Hierarchy Process (AHP) method. (2) The employed approach is qualitative and follows the AHP procedure to select the most suitable option that can be applied to cybersecurity. This selection process evaluated different criteria in five standards: ISO/IEC 27005:2022, NIST SP 800-30, OCTAVE-S, MAGERIT, and EBIOS-RM. (3) The AHP method enabled, in a practical manner, the selection of OCTAVE-S as the primary methodology, complemented with elements from other standards. Finally, the proposed methodology was implemented in a cloud-based web application called the Risk Analysis Module, integrated into the Keru IT security platform. It is concluded that the multi-criteria AHP method is effective and allows organizations to select the standards most appropriate to their needs, with potential applicability to other types of decisions. Full article

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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41 pages, 6103 KB

Open AccessArticle

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 1550

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 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)

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28 pages, 4127 KB

Open AccessFeature PaperArticle

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

Cited by 1 | Viewed by 2341

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 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)

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21 pages, 1658 KB

Open AccessArticle

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 3108

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 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)

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81 pages, 2927 KB

Open AccessSystematic Review

Behavioral Biometric Continuous Authentication for Mobile Devices with an Intelligent Personal Agent: A Systematic Review

by Madi Gali, Aray Kassenkhan, Yersain Chinibayev, Aigerim Abshukirova and Vassiliy Serbin

Technologies 2026, 14(7), 451; https://doi.org/10.3390/technologies14070451 - 22 Jul 2026

Viewed by 220

Abstract

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments. Full article

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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34 pages, 1728 KB

Open AccessSystematic 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 1370

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 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)

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