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Future Internet
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Information and Future Internet Security, Trust and Privacy—4th Edition
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Information and Future Internet Security, Trust and Privacy—4th Edition

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 6622

Special Issue Editors

Prof. Dr. Weizhi Meng

E-Mail Website
Guest Editor
School of Computing and Communications, Lancaster University, Lancashire LA1 4YW, UK
Interests: cyber security; intrusion detection; mobile security and authentication; HCI security; malware analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christian D. Jensen

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, 8200 Aarhus, Denmark
Interests: security in ubiquitous computing; secure collaboration in open dynamic systems; pervasive computing environments; sensor networks and the Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the Internet of Things (IoT) enables billions of Internet-connected devices, e.g., smart sensors, to communicate and interact with each other over the network/Internet worldwide. IoT can offer remote monitoring and control, and is now being adopted in many domains. For example, it is the basis for smart cities, helping to achieve a better quality of life and a lower consumption of resources. In addition, smartphones are the most commonly used IoT devices, and can help control washing machines, refrigerators, or cars. However, the IoT also faces many challenges concerning information and Internet security. For example, attackers can impersonate a relay node, compromising the integrity of information during communications. When they control or infect several internal nodes in an IoT network, the security of the whole distributed environment would be greatly threatened. Therefore, there is a need to safeguard information and the Internet environment against the plethora of modern external and internal threats.

This Special Issue will focus on information and Internet security in an attempt to solicit the latest technologies, solutions, case studies, and prototypes surrounding this topic.

Prof. Dr. Weizhi Meng
Prof. Dr. Christian D. Jensen
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. Future Internet 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

  • network security
  • trust management
  • intrusion detection
  • SDN security
  • data privacy
  • internet security
  • trust aggregation
  • blockchain in security and trust
  • AI in trust
  • critical system security

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Related Special Issues

Published Papers (7 papers)

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Research

Jump to: Review

26 pages, 1912 KB  
Article
A Temporally Dynamic Feature-Extraction Framework for Phishing Detection with LIME and SHAP Explanations
by Chris Mayo, Michael Tchuindjang, Sarfraz Brohi and Nikolaos Ersotelos
Future Internet 2026, 18(2), 101; https://doi.org/10.3390/fi18020101 - 14 Feb 2026
Viewed by 627
Abstract
Phishing remains one of the most pervasive social engineering threats, exploiting human vulnerabilities and continuously evolving to bypass static detection mechanisms. Existing machine learning models achieve high accuracy but often act as opaque systems that lack robustness to evolving tactics and explainability, limiting [...] Read more.
Phishing remains one of the most pervasive social engineering threats, exploiting human vulnerabilities and continuously evolving to bypass static detection mechanisms. Existing machine learning models achieve high accuracy but often act as opaque systems that lack robustness to evolving tactics and explainability, limiting trust and real-world deployment. In this research, we propose a dynamic Explainable AI (XAI) approach for phishing detection that integrates temporally aware feature extraction with dual interpretability through LIME and SHAP applied to the resulting window-level features. The novelty of this research lies in a temporally dynamic feature framework that simulates a plausible email reading progression using a heuristic temporal model and employs a sliding window aggregation method to capture behavioural and temporal patterns within email content. Using an aggregated dataset of 82,500 phishing and legitimate emails, dynamic features were extracted and used to train four classifiers: Random Forest, XGBoost, Multi-Layer Perceptron, and Logistic Regression. Ensemble models demonstrated strong performance with XGBoost achieving 94% accuracy and Random Forest 93%. This research addresses an important gap by combining dynamically constructed temporal features with transparent explanations, achieving high detection performance while preserving interpretability. These findings demonstrate that dynamic temporal modelling with explainable learning can enhance the trustworthiness and practicality of phishing detection systems, highlighting that temporally structured features and explainable learning can enhance the trustworthiness and practical deployability of phishing detection systems without incurring excessive computational overhead. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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19 pages, 1747 KB  
Article
Video Deepfake Detection Based on Multimodality Semantic Consistency Fusion
by Fang Sun, Xiaoxuan Guo, Tong Zhang, Yang Liu and Jing Zhang
Future Internet 2026, 18(2), 67; https://doi.org/10.3390/fi18020067 - 23 Jan 2026
Viewed by 676
Abstract
Deepfake detection in video data typically relies on mining deep embedded representations across multiple modalities to obtain discriminative fused features and thereby improve detection accuracy. However, existing approaches predominantly focus on how to exploit complementary information across modalities to ensure effective fusion, while [...] Read more.
Deepfake detection in video data typically relies on mining deep embedded representations across multiple modalities to obtain discriminative fused features and thereby improve detection accuracy. However, existing approaches predominantly focus on how to exploit complementary information across modalities to ensure effective fusion, while often overlooking the impact of noise and interference present in the data. For instance, issues such as small objects, blurring, and occlusions in the visual modality can disrupt the semantic consistency of the fused features. To address this, we propose a Multimodality Semantic Consistency Fusion model for video forgery detection. The model introduces a semantic consistency gating mechanism to enhance the embedding of semantically aligned information across modalities, thereby improving the discriminability of the fused representations. Furthermore, we incorporate an event-level weakly supervised loss to strengthen the global semantic discrimination of the video data. Extensive experiments on standard video forgery detection benchmarks demonstrate the effectiveness of the proposed method, achieving superior performance in both forgery event detection and localization compared to state-of-the-art approaches. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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23 pages, 3127 KB  
Article
Heterogeneous Federated Learning via Knowledge Transfer Guided by Global Pseudo Proxy Data
by Wenhao Sun, Xiaoxuan Guo, Wenjun Liu and Fang Sun
Future Internet 2026, 18(1), 36; https://doi.org/10.3390/fi18010036 - 8 Jan 2026
Viewed by 596
Abstract
Federated learning with data free knowledge distillation enables effective and privacy-preserving knowledge aggregation by employing generators to produce local pseudo samples during client-side model migration. However, in practical applications, data distributions across different institutions are often non-independent and identically distributed (Non-IID), which introduces [...] Read more.
Federated learning with data free knowledge distillation enables effective and privacy-preserving knowledge aggregation by employing generators to produce local pseudo samples during client-side model migration. However, in practical applications, data distributions across different institutions are often non-independent and identically distributed (Non-IID), which introduces bias in local models and consequently impedes the effective transfer of knowledge to the global model. In addition, insufficient local training can further exacerbate model bias, undermining overall performance. To address these challenges, we propose a heterogeneous federated learning framework that enhances knowledge transfer through guidance from global proxy data. Specifically, a noise filter is incorporated into the training of local generators to mitigate the negative impact of low-quality pseudo proxy samples on local knowledge distillation. Furthermore, a global generator is introduced to produce global pseudo proxy samples, which, together with local pseudo proxy data, are used to construct a cross attention matrix. This design effectively alleviates overfitting and underfitting issues in local models caused by data heterogeneity. Extensive experiments on publicly available datasets with heterogeneous data distributions demonstrate the superiority of the proposed framework. Results show that when the Dirichlet distribution coefficient is 0.05, our method achieves an average accuracy improvement of 5.77% over popular baselines; when the coefficient is 0.1, the improvement reaches 6.54%. Even under uniformly distributed sample classes, our model still achieves an average accuracy improvement of 7.07% compared to other methods. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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21 pages, 886 KB  
Article
A Dual-Attention CNN–GCN–BiLSTM Framework for Intelligent Intrusion Detection in Wireless Sensor Networks
by Laith H. Baniata, Ashraf ALDabbas, Jaffar M. Atwan, Hussein Alahmer, Basil Elmasri and Chayut Bunterngchit
Future Internet 2026, 18(1), 5; https://doi.org/10.3390/fi18010005 - 22 Dec 2025
Viewed by 840
Abstract
Wireless Sensor Networks (WSNs) are increasingly being used in mission-critical infrastructures. In such applications, they are evaluated on the risk of cyber intrusions that can target the already constrained resources. Traditionally, Intrusion Detection Systems (IDS) in WSNs have been based on machine learning [...] Read more.
Wireless Sensor Networks (WSNs) are increasingly being used in mission-critical infrastructures. In such applications, they are evaluated on the risk of cyber intrusions that can target the already constrained resources. Traditionally, Intrusion Detection Systems (IDS) in WSNs have been based on machine learning techniques; however, these models fail to capture the nonlinear, temporal, and topological dependencies across the network nodes. As a result, they often suffer degradation in detection accuracy and exhibit poor adaptability against evolving threats. To overcome these limitations, this study introduces a hybrid deep learning-based IDS that integrates multi-scale convolutional feature extraction, dual-stage attention fusion, and graph convolutional reasoning. Moreover, bidirectional long short-term memory components are embedded into the unified framework. Through this combination, the proposed architecture effectively captures the hierarchical spatial–temporal correlations in the traffic patterns, thereby enabling precise discrimination between normal and attack behaviors across several intrusion classes. The model has been evaluated on a publicly available benchmarking dataset, and it has been found to attain higher classification capability in multiclass scenarios. Furthermore, the model outperforms conventional IDS-focused approaches. In addition, the proposed design aims to retain suitable computational efficiency, making it appropriate for edge and distributed deployments. Consequently, this makes it an effective solution for next-generation WSN cybersecurity. Overall, the findings emphasize that combining topology-aware learning with multi-branch attention mechanisms offers a balanced trade-off between interpretability, accuracy, and deployment efficiency for resource-constrained WSN environments. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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24 pages, 1582 KB  
Article
Future Internet Applications in Healthcare: Big Data-Driven Fraud Detection with Machine Learning
by Konstantinos P. Fourkiotis and Athanasios Tsadiras
Future Internet 2025, 17(10), 460; https://doi.org/10.3390/fi17100460 - 8 Oct 2025
Cited by 2 | Viewed by 1783
Abstract
Hospital fraud detection has often relied on periodic audits that miss evolving, internet-mediated patterns in electronic claims. An artificial intelligence and machine learning pipeline is being developed that is leakage-safe, imbalance aware, and aligned with operational capacity for large healthcare datasets. The preprocessing [...] Read more.
Hospital fraud detection has often relied on periodic audits that miss evolving, internet-mediated patterns in electronic claims. An artificial intelligence and machine learning pipeline is being developed that is leakage-safe, imbalance aware, and aligned with operational capacity for large healthcare datasets. The preprocessing stack integrates four tables, engineers 13 features, applies imputation, categorical encoding, Power transformation, Boruta selection, and denoising autoencoder representations, with class balancing via SMOTE-ENN evaluated inside cross-validation folds. Eight algorithms are compared under a fraud-oriented composite productivity index that weighs recall, precision, MCC, F1, ROC-AUC, and G-Mean, with per-fold threshold calibration and explicit reporting of Type I and Type II errors. Multilayer perceptron attains the highest composite index, while CatBoost offers the strongest control of false positives with high accuracy. SMOTE-ENN provides limited gains once representations regularize class geometry. The calibrated scores support prepayment triage, postpayment audit, and provider-level profiling, linking alert volume to expected recovery and protecting investigator workload. Situated in the Future Internet context, this work targets internet-mediated claim flows and web-accessible provider registries. Governance procedures for drift monitoring, fairness assessment, and change control complete an internet-ready deployment path. The results indicate that disciplined preprocessing and evaluation, more than classifier choice alone, translate AI improvements into measurable economic value and sustainable fraud prevention in digital health ecosystems. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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Review

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30 pages, 721 KB  
Review
A Review of Honeypots: Fingerprinting Techniques, Detection, and Evasion Mechanisms
by Arooj Chaudhry, Casper Andersen, Gaurav Choudhary and Nicola Dragoni
Future Internet 2026, 18(4), 190; https://doi.org/10.3390/fi18040190 - 1 Apr 2026
Viewed by 271
Abstract
Honeypot fingerprinting poses a significant threat in cybersecurity, as attackers who are able to identify honeypot systems can successfully evade them, thereby greatly reducing their overall effectiveness as defensive and intelligence-gathering tools. Over the years, numerous studies have proposed a variety of analytical [...] Read more.
Honeypot fingerprinting poses a significant threat in cybersecurity, as attackers who are able to identify honeypot systems can successfully evade them, thereby greatly reducing their overall effectiveness as defensive and intelligence-gathering tools. Over the years, numerous studies have proposed a variety of analytical techniques and countermeasures to minimize honeypot fingerprinting and improve honeypot stealth. This paper presents a comprehensive examination of the methods and strategies that attackers employ to detect and fingerprint honeypot systems, including behavioural, network-based, and system-level indicators. In addition, this paper analyzes common vulnerabilities inherent in both low-interaction and high-interaction honeypots that facilitate successful fingerprinting. Existing anti-detection and obfuscation techniques are evaluated for their effectiveness and limitations. Specifically, this paper offers a structured analysis of honeypot fingerprinting techniques, examines attackers’ probing strategies, evaluates the most vulnerable protocol artifacts, and outlines mitigation strategies to reduce the likelihood of honeypot detection. Finally, this paper discusses how emerging technologies and increasingly complex computing environments, such as cloud infrastructure and virtualization, impact honeypot deployment, and it highlights open challenges and promising future research directions in the field of honeypot anti-fingerprinting. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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31 pages, 1065 KB  
Review
Survey on Biometric Authentication for Decentralized Identity Management: Trends, Challenges, and Future Directions
by Imen Rjab and Layth Sliman
Future Internet 2026, 18(3), 126; https://doi.org/10.3390/fi18030126 - 2 Mar 2026
Viewed by 1170
Abstract
Decentralized Identity (DID) systems aim to restore user control over digital identities by minimizing reliance on centralized authorities. However, ensuring secure identity management in distributed environments remains a significant challenge. Biometric authentication offers a compelling solution by leveraging unique, non-transferable human traits to [...] Read more.
Decentralized Identity (DID) systems aim to restore user control over digital identities by minimizing reliance on centralized authorities. However, ensuring secure identity management in distributed environments remains a significant challenge. Biometric authentication offers a compelling solution by leveraging unique, non-transferable human traits to enhance security and usability compared to traditional methods such as passwords or tokens. Integrating biometrics into DID frameworks represents an important step toward privacy-preserving, user-centric identity verification aligned with the principles of decentralization. Despite growing interest in both biometrics and DIDs, their integration remains largely underexplored in the literature, with hardly any survey providing a systematic analysis of this convergence. This work addresses this gap by presenting a comprehensive review of biometric-enabled DID systems, examining their architectures, potential, and limitations. It emphasizes the role of multimodal biometrics in enhancing accuracy, inclusiveness, and resistance to spoofing, while highlighting key challenges related to data immutability, privacy preservation, interoperability, and regulatory compliance. Overall, this survey establishes a structured foundation for future research on secure, scalable, and privacy-preserving biometric-enabled decentralized identity frameworks. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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