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 427

Special Issue Editors


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Guest Editor

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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)
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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

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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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Published Papers (1 paper)

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Research

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
Viewed by 290
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
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