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Future Internet

Future Internet is an international, peer-reviewed, open access journal on internet technologies and the information society, published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Computer Science, Information Systems) / CiteScore - Q1 (Computer Networks and Communications)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15 days after submission; acceptance to publication is undertaken in 3.7 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: Reviewers whose reports are timely and of high quality receive an APC discount voucher for a future publication in an MDPI journal. Become a reviewer.
Journal Clusters of Network and Communications Technology: Future Internet, IoT, Telecom, Journal of Sensor and Actuator Networks, Network, Signals.

Impact Factor: 4.6 (2025); 5-Year Impact Factor: 3.9 (2025)

Imprint Information Journal Flyer Open Access ISSN: 1999-5903

Latest Articles

48 pages, 27955 KB

Open AccessArticle

An Explainable AI Framework for Identity Document Authentication in AML/KYC Verification

by Eldeena Huey Yinn Lim and Tee Connie

Future Internet 2026, 18(9), 485; https://doi.org/10.3390/fi18090485 - 16 Sep 2026

This study investigates the development of an AI-driven document authentication framework for Anti-Money Laundering (AML) and Know Your Customer (KYC) verification environments. Conventional manual inspection and rule-based verification techniques often fail to detect sophisticated forged identity documents containing subtle visual or semantic manipulations. [...] Read more.

This study investigates the development of an AI-driven document authentication framework for Anti-Money Laundering (AML) and Know Your Customer (KYC) verification environments. Conventional manual inspection and rule-based verification techniques often fail to detect sophisticated forged identity documents containing subtle visual or semantic manipulations. To address this limitation, the proposed framework combines handcrafted forensic feature extraction, OCR-driven semantic analysis, rule-based semantic field extraction and Random Forest classification to identify inconsistencies within identity documents captured under realistic mobile imaging conditions. Experimental evaluation was conducted using selected MIDV-2020 identity document subsets consisting of Albanian identity cards, Latvian passports, and Slovakian identity cards. The proposed framework achieved a recall rate of 92.31% and an overall accuracy of 84.85% on the held-out test set, while maintaining interpretable forensic feature analysis suitable for regulated AML/KYC environments. The results demonstrate that lightweight and explainable machine learning approaches can provide effective forged-document detection without requiring computationally intensive deep learning architectures. Full article

(This article belongs to the Special Issue Securing Artificial Intelligence Against Attacks)

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27 pages, 10423 KB

Open AccessArticle

Understanding Differential Privacy in Decentralized Federated Learning: A Controlled Privacy–Utility Comparison

by Alsharif Hasan Mohamad Aburbeian, Manuel Fernández-Veiga, Ana Fernández-Vilas, Majdi Owda and Amani Yousef Owda

Future Internet 2026, 18(9), 484; https://doi.org/10.3390/fi18090484 - 16 Sep 2026

Centralized Federated Learning (FL) enables collaborative model training without sharing raw data. Differential privacy (DP) is widely used to protect sensitive information in FL; however, its behavior in decentralized environments remains poorly understood. This study empirically compares centralized FL and sequential Decentralized Federated [...] Read more.

Centralized Federated Learning (FL) enables collaborative model training without sharing raw data. Differential privacy (DP) is widely used to protect sensitive information in FL; however, its behavior in decentralized environments remains poorly understood. This study empirically compares centralized FL and sequential Decentralized Federated Learning (DFL) under matched clipping and perturbation settings to examine model utility and privacy leakage. Both frameworks were evaluated under a common experimental setup with non-IID data, and each configuration was evaluated across five independent seeds. Utility was evaluated under matched experimental perturbation parameters, whereas formal client-level privacy accounting was applied to the perturbed round-end model releases, with each client’s complete dataset treated as the protected unit. Empirical leakage was evaluated separately using membership inference and gradient inversion attacks. Within the evaluated MNIST configuration, the results show that clipping, perturbation, and the learning procedure jointly influence the observed privacy–utility behavior. At

C = 1

and

ϵ_{cal} = 2

, the mean final accuracy was

73.83 %

for FL and

97.74 %

for sequential DFL. At

ϵ_{cal} = 4

, InvGrad reconstruction for FL produced an MSE of

0.048

, PSNR of

13.71

, and SSIM of

0.280

, compared with

0.130

,

10.10

, and

0.184

for sequential DFL, respectively. Future research will investigate topology-aware privacy mechanisms and adaptive noise allocation for decentralized systems. Full article

(This article belongs to the Special Issue Privacy-Preserving and Secure Machine Learning)

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20 pages, 1210 KB

Open AccessArticle

On the Role of Data Normalization in Adversarial Robustness Evaluation: A Comparative Analysis of XGBoost and Random Forest for IoT Network Intrusion Detection

by Inas Mostefai, Sofiane Boukli-Hacene and Abdelhafid Abouaissa

Future Internet 2026, 18(9), 483; https://doi.org/10.3390/fi18090483 - 16 Sep 2026

Intrusion detection systems (IDS) based on machine learning (ML) have demonstrated remarkable effectiveness in detecting cyber-attacks in Internet of Things (IoT) environments. Despite their high predictive performance, the robustness of these systems against adversarial attacks remains insufficiently understood, particularly with regard to the [...] Read more.

Intrusion detection systems (IDS) based on machine learning (ML) have demonstrated remarkable effectiveness in detecting cyber-attacks in Internet of Things (IoT) environments. Despite their high predictive performance, the robustness of these systems against adversarial attacks remains insufficiently understood, particularly with regard to the influence of data pre-processing techniques such as feature normalisation. This study examines the impact of Min-Max normalisation on the robustness of two widely used tree-based classifiers, XGBoost and Random Forest, against adversarial attacks of the Fast Gradient Method (FGM) and Projected Gradient Descent (PGD) types. Using the ACI-IoT-2023 dataset, we conduct comprehensive experiments across three scenarios: models trained on normalized data and evaluated using a fixed perturbation magnitude (

ϵ

), models trained on raw data and evaluated using the same fixed

ϵ

, and models trained on raw data and evaluated using an adaptive

ϵ

calibrated to the scale of each feature. Our results demonstrate that Min-Max normalization alone does not inherently make these models robust against adversarial attacks. The apparent robustness observed on non-normalised data with a fixed perturbation budget is an artefact caused by a misalignment in the scaling of the perturbation. When using an adaptive epsilon strategy calibrated according to feature scales, the accuracies of XGBoost and Random Forest decreased to 2.30% and 2.42% respectively under the

L_{\infty}

norm with the PGD attack. These results highlight the crucial importance of taking feature scaling into account when assessing robustness against adversarial attacks and provide practical recommendations for the design of reliable evaluation protocols for intrusion detection systems in the Internet of Things. Full article

(This article belongs to the Section Internet of Things)

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3 pages, 129 KB

Open AccessEditorial

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) for a Hyperconnected World

by Faycal Bouhafs and Sachin Sharma

Future Internet 2026, 18(9), 482; https://doi.org/10.3390/fi18090482 - 15 Sep 2026

Data networks are a fundamental infrastructure of modern societies, supporting industries, transport, communication, government services, finance, etc [...] Full article

(This article belongs to the Special Issue Software-Defined Networking (SDN) and Network Function Virtualization (NFV) for a Hyperconnected World)

27 pages, 472 KB

Open AccessArticle

From Semantic Retrieval to Conversational Agent: A Web-Based RAG Architecture for Interactive System Dynamics Modeling

by Pavel Kyurkchiev and Anton Iliev

Future Internet 2026, 18(9), 481; https://doi.org/10.3390/fi18090481 - 15 Sep 2026

Keyword-based semantic search performs poorly on complex knowledge repositories such as System Dynamics model databases, where users know the behavior they want to simulate but not the structural vocabulary needed to retrieve it. We replace the static search field with an interactive web-based [...] Read more.

Keyword-based semantic search performs poorly on complex knowledge repositories such as System Dynamics model databases, where users know the behavior they want to simulate but not the structural vocabulary needed to retrieve it. We replace the static search field with an interactive web-based conversational agent, built as a Retrieval-Augmented Generation architecture in which the system asks context-aware clarifying questions to narrow the search scope across multiple turns. The architecture was evaluated in an ablation study of 37 benchmark scenarios over a curated corpus of 63 models, comparing six retrieval strategies against an expert semantic baseline using Precision@5, Recall@5, MRR@5, nDCG@5 and Hit@5. Conversational refinement raised mean nDCG@5 from 0.1066 to 0.4422 and Hit@5 from 0.1892 to 0.5946. The improvement over the broad-intent baseline is significant on nDCG@5 and MRR@5 under Holm-corrected Wilcoxon signed-rank tests. This comparison aggregates the clarification exchange with the additional user input it elicits. A separate condition that bypasses the generative rewriting step bounds the contribution of that step. A residual gap to the expert semantic baseline (nDCG@5 = 0.5750) remains and is significant on MRR@5. Lexical BM25 applied to expert queries outperformed dense retrieval on every metric (nDCG@5 = 0.8053), showing that sparse matching retains a decisive advantage where the structural vocabulary is exact. We conclude that conversational elicitation is an effective mechanism for narrowing the expertise gap in this domain, and that the lexical results motivate pairing it with hybrid sparse–dense retrieval. Full article

(This article belongs to the Special Issue Human-Centered Artificial Intelligence—2nd Edition)

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16 pages, 791 KB

Open AccessArticle

Privacy-Preserving Information Fusion of Heterogeneous Cross-Jurisdictional Sources for Traffic Accident Severity Prediction

by Ashik Shah Jahangeer and Shanmugavadivu Pichai

Future Internet 2026, 18(9), 480; https://doi.org/10.3390/fi18090480 - 14 Sep 2026

Road safety authorities each hold accident records that, when combined, could train stronger severity prediction models, yet these records can be neither centralized, for privacy and governance reasons, nor naively merged, because jurisdictions encode severity under incompatible ontologies. This paper recasts that impasse [...] Read more.

Road safety authorities each hold accident records that, when combined, could train stronger severity prediction models, yet these records can be neither centralized, for privacy and governance reasons, nor naively merged, because jurisdictions encode severity under incompatible ontologies. This paper recasts that impasse as an information fusion problem and fuses model updates from multiple road safety data silos into a single severity model while every raw record stays at its source. Three components act together: model-level fusion under differential privacy, a reliability-weighted aggregation rule that trusts each source based on its measured quality rather than its size, and a per-source centered logit adjustment layer that reconciles mismatched label priors without double-correcting the shared class imbalance. The primary evaluation is a clean cross-silo setting: five United States state datasets (US Accidents) that share one severity ontology but are held by distinct custodians. Here, over five seeds with 95% confidence intervals, effect sizes, and Holm–Bonferroni correction, private fusion recovers most of a centralized upper bound while keeping data local (

0.599

balanced accuracy versus

0.624

when centralized and

0.544

when local-only), and reliability-weighted fusion attains the highest macro F1 of all methods (

0.567

). Reliability weighting yields a small but consistent robustness advantage under privacy noise; in leave-one-state-out transfer, its improvement over uniform averaging is large on every held-out state but, after Holm correction, survives in two out of five cases. Crucially, we also report a boundary honestly; a United Kingdom source that encodes injury severity—an ontologically different target from the US traffic impact scale—is used as a deliberate out-of-ontology transfer stress test, and a transfer to it collapses to chance (

0.50

,

p = 0.62

). Two further honest results are reported: alignment raises accuracy everywhere but does not close the across-source gap, and a membership inference attack reveals no measurable leakage for differential privacy to remove. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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35 pages, 9748 KB

Open AccessReview

Digital Twin in Vehicular Communications: Challenges and Opportunities

by Junliang Ye, Yuna Jiang, Ziwei Chen, Zijing He, Deqiao Gan, Xiaomeng Ai, Ling Ma and Xiaohu Ge

Future Internet 2026, 18(9), 479; https://doi.org/10.3390/fi18090479 - 14 Sep 2026

Digital twins (DTs) are increasingly studied together with vehicular communications, but the literature spans different twinned entities, synchronization assumptions, computing placements, and levels of experimental evidence, which makes results difficult to compare. This survey provides a communication-centric review of DT-enabled vehicular systems. We [...] Read more.

Digital twins (DTs) are increasingly studied together with vehicular communications, but the literature spans different twinned entities, synchronization assumptions, computing placements, and levels of experimental evidence, which makes results difficult to compare. This survey provides a communication-centric review of DT-enabled vehicular systems. We first introduce an operational definition for a vehicular DT and distinguish a synchronized DT from a static simulator or digital model. We then organize the field according to twin granularity, physical–virtual synchronization, vehicle–edge–cloud placement, enabling V2X technologies, and measurable evaluation dimensions. The standardization discussion is updated from DSRC and IEEE 802.11p through 5G-Advanced and ongoing 3GPP work toward 6G, together with recent digital-twin and network-digital-twin standards. Rather than treating reported benefits as established outcomes, we compare the assumptions, metrics, evidence, limitations, deployment constraints, and safety and security implications of representative studies. Finally, we identify research priorities involving synchronization staleness, twin placement and migration, communication–computation–fidelity tradeoffs, validation and uncertainty, interoperable data models, lifecycle management, and safety-aware closed-loop operation. The survey is intended to provide researchers and practitioners with a structured basis for deciding when DT techniques are appropriate for vehicular communication systems and how such systems should be evaluated. Full article

(This article belongs to the Special Issue Progress and Challenges in Wireless Communication)

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21 pages, 9045 KB

Open AccessReview

The Impact of Intelligent Transport Systems on Safety, Emissions Reduction, and Travel Time: A Review

by Nadica Stojanovic, Ivan Grujic, Suzana Petrovic Savic, Miladin Stefanovic and Aleksandar Djordjevic

Future Internet 2026, 18(9), 478; https://doi.org/10.3390/fi18090478 - 14 Sep 2026

The intensive development of road transportation and the increasing number of vehicles have led to significant challenges related to road safety, traffic congestion, travel time, energy consumption, and negative environmental impacts. In this context, intelligent transport systems (ITS) represent a significant approach to [...] Read more.

The intensive development of road transportation and the increasing number of vehicles have led to significant challenges related to road safety, traffic congestion, travel time, energy consumption, and negative environmental impacts. In this context, intelligent transport systems (ITS) represent a significant approach to improving the efficiency and sustainability of modern transportation systems. The aim of this paper is to present and systematize the application of modern ITS technologies for improving road safety, reducing emissions, and shortening travel time. Based on an analysis of the relevant literature, the fundamental components and architecture of ITS are presented, including sensor systems, V2X communication, IoT, cloud and edge computing, as well as the application of artificial intelligence in traffic data processing and prediction. The analyzed studies demonstrate that ITS enables dynamic traffic flow management, route optimization, reduction in congestion and emissions, and more efficient responses to emergency situations. Particular attention is devoted to the possibility of simultaneously considering travel time, energy consumption, emissions, noise, and road safety. As a synthesis of the analyzed findings, an integrated algorithm for intelligent traffic management is proposed, operating as a closed feedback loop encompassing data collection, state assessment, prediction, optimization, and control. Future ITS development is expected to focus on the integration of AI, IoT, 6G, and edge computing technologies and their validation using real-world traffic data. Full article

(This article belongs to the Special Issue Next-Generation Intelligent Transportation Systems)

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27 pages, 1225 KB

Open AccessArticle

FinDS-Agent: A Cloud–Edge Collaborative Data Science Agent for Financial Analytics

by Xiaozheng Du, Ruijun Deng, Cheng Wang, Feng Zhou, Shijing Hu, Zhihui Lu and Simon Fong

Future Internet 2026, 18(9), 477; https://doi.org/10.3390/fi18090477 - 13 Sep 2026

Large language model agents can automate data science workflows, but cloud-centric deployment exposes sensitive context and edge-only deployment limits analytical capability. We present FinDS-Agent, a cloud–edge framework that keeps raw records and program execution at the trusted edge while providing a policy-screened, sanitized [...] Read more.

Large language model agents can automate data science workflows, but cloud-centric deployment exposes sensitive context and edge-only deployment limits analytical capability. We present FinDS-Agent, a cloud–edge framework that keeps raw records and program execution at the trusted edge while providing a policy-screened, sanitized context to support cloud planning. FinDS-Agent integrates a Three-Stage Cascaded Privacy Gate (TCPG), a Multi-Dimensional Joint Router (MJR), contract-guided ToolGraph planning, edge-side verification, and bounded repair. On 222 DataSciBench tasks over three runs, FinDS-Agent achieved a 69.93% completion rate and 57.06% success rate, improving over Edge-Only by 9.50 and 5.71 percentage points while invoking the cloud for 32.27% of eligible task-runs. On FinDS-Privacy-Bench, TCPG increased sensitive-field recall from 58.20% to 98.10%; no payload-leakage event was observed in the full set (0/200; Wilson 95% CI: 0–1.8845%) or blind split (0/100; 0–3.6993%) under the specified audit and threat model. External evaluation gave pass rates of 33.2%, 53.1%, and 62.3% for Edge-Only, FinDS-Agent, and Cloud-Only on DS-1000. These empirical results support selective cloud planning while delimiting statistical, privacy, and transfer claims. Full article

(This article belongs to the Special Issue LLM-Driven Agentic AI in Edge-Cloud Computing)

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21 pages, 4446 KB

Open AccessArticle

AI-Enabled Smart Monitoring of Bovine Embryo Development Using Time-Lapse Imaging and Transfer Learning

by Manickavasagan Shivaani, Meenakshi P.L and Pavneesh Madan

Future Internet 2026, 18(9), 476; https://doi.org/10.3390/fi18090476 - 12 Sep 2026

Time-lapse incubation systems enable the continuous, non-invasive monitoring of embryonic development; however, identifying developmental stages still requires substantial manual assessment, making the process time-consuming, labor-intensive, and potentially subjective. This study evaluated the developmental kinetics of bovine embryos cultured in synthetic oviductal fluid (SOF) [...] Read more.

Time-lapse incubation systems enable the continuous, non-invasive monitoring of embryonic development; however, identifying developmental stages still requires substantial manual assessment, making the process time-consuming, labor-intensive, and potentially subjective. This study evaluated the developmental kinetics of bovine embryos cultured in synthetic oviductal fluid (SOF) and Gx-TL™ media using MIRI time-lapse imaging, and investigated the effectiveness of transfer learning-based convolutional neural networks (CNNs) for automated embryo-stage classification. A total of 311 zygotes were individually cultured under standard in vitro fertilization conditions, including 152 embryos in SOF medium and 159 embryos in Gx-TL™ medium. In the SOF group, 81 embryos reached the two-cell stage, 10 developed to the morula stage, and 6 reached the blastocyst stage. In the Gx-TL™ group, 77 embryos reached the two-cell stage, 18 developed to the morula stage, and 10 reached the blastocyst stage. Time-lapse images were manually annotated according to key developmental stages, including the two-cell through eight-cell stages, morula, and blastocyst. The annotated image dataset was augmented to 5000 images and used to train three pretrained CNN architectures: ResNet18, DenseNet121, and EfficientNet-B0. All three models achieved 100% accuracy in the two-class classification task across both culture media. Classification accuracy ranged from 95% to 100% for the nine-class model and from 98% to 100% for the ten-class model. These findings provide preliminary evidence that transfer learning-based convolutional neural networks (CNNs) can support the automated classification of bovine embryonic developmental stages using time-lapse images. Full article

(This article belongs to the Special Issue Machine Learning, Big Data, and Artificial Intelligence in Smart Systems)

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50 pages, 5536 KB

Open AccessArticle

Technical and Regulatory Prerequisites for Blockchain-Enabled Point-of-Sale Systems: A Tanzanian Case Study

by Julius Massawe, Bonny Mgawe, Cleverence Kombe and Anael Sam

Future Internet 2026, 18(9), 475; https://doi.org/10.3390/fi18090475 - 12 Sep 2026

The continued expansion of digital payment technologies has encouraged Tanzanian district councils to use Point-of-Sale (POS) systems to collect service fees. In current POS systems, authorized POS terminals capture payment details, and through the centralized server, transactions are recorded on the POS database, [...] Read more.

The continued expansion of digital payment technologies has encouraged Tanzanian district councils to use Point-of-Sale (POS) systems to collect service fees. In current POS systems, authorized POS terminals capture payment details, and through the centralized server, transactions are recorded on the POS database, with receipts printed as confirmation. Although this architecture supports recording and monitoring transaction revenue, it provides limited support for verifying the identity of the actor authorizing the transaction and for independent confirmation of transaction integrity during auditing. To address these limitations, this study investigated the essential requirements, standards, and protocols for integrating a Self-Sovereign Identity (SSI) as a blockchain-based identity solution with existing POS systems and for using a permissioned blockchain platform to verify integrity. To achieve the study objectives, an exploratory qualitative approach was used, involving 32 semi-structured interviews with POS operators, revenue accountants, internal auditors, Information and Communication Technology (ICT) administrators, a regulator, and blockchain experts. A hybrid deductive–inductive thematic analysis was used to establish three requirement themes, namely, security and identity management, legal and regulatory compliance, and data management and integrity assurance; two standard themes, namely, security and cryptographic standards, and identity and decentralized identification; and two protocol themes, namely, security and user authentication protocols, and data management and identity portability protocols. The findings were mapped to applicable legal obligations, compliance standards, technical specifications, and implementation controls, indicating how these requirements were translated into the conceptual SSI-POS integration for district-council POS systems. The proposed solution separates credential issuance, credential holder, and device POS management; verifier and POS transaction processing; blockchain and integrity evidence; and the assurance domain across defined trust boundaries. After a signed transaction is approved by the verifier, a complete transaction receipt remains in the existing POS database, while receipt hashes or the corresponding Merkle roots are anchored on the permissioned blockchain to provide tamper-evident verification. Hyperledger Besu with Quorum Byzantine Fault Tolerance (QBFT) was selected for its fit with permissioned, multi-organizational governance and for independent replication of receipt-hash evidence. The study provides a stakeholder-derived, regulatory-aligned conceptual foundation for SSI-POS integration without replacing the existing POS system workflow. Full article

(This article belongs to the Section Cybersecurity)

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18 pages, 1515 KB

Open AccessArticle

Intelligent Synchronization of Machine Learning Models Using Graph Neural Networks: Application to Flood Prediction

by Boban Temelkovski, Rexhep Mustafovski, Jugoslav Achkoski, Georgi Dimirovski and Mile Stankovski

Future Internet 2026, 18(9), 474; https://doi.org/10.3390/fi18090474 - 11 Sep 2026

Flood prediction remains a critical challenge in environmental risk management and disaster preparedness. Accurate river-level forecasting is essential for the development of reliable early warning systems and the mitigation of flood-related risks. However, conventional ensemble approaches, such as averaging and majority voting, often [...] Read more.

Flood prediction remains a critical challenge in environmental risk management and disaster preparedness. Accurate river-level forecasting is essential for the development of reliable early warning systems and the mitigation of flood-related risks. However, conventional ensemble approaches, such as averaging and majority voting, often exhibit limited adaptability when individual models respond differently to anomalies or incomplete data. To address this limitation, this study proposes a graph-based synchronization framework that integrates XGBoost and Random Forest models using a Graph Convolutional Network (GCN). The proposed framework represents the outputs of the base prediction models as graph nodes and employs graph message passing to learn context-dependent relationships between their predictions. The framework is evaluated using real-world hydrological observations from the Lepenec River Basin in North Macedonia together with meteorological data obtained from the OpenWeatherMap API. Experimental results demonstrate that the proposed GCN-based synchronization framework outperforms both the standalone prediction models and the previously proposed linear synchronization method, achieving an R² value of 0.91 and a Mean Absolute Error (MAE) of 0.21. The obtained results indicate that graph-based synchronization provides an adaptive approach for integrating heterogeneous machine-learning models and has the potential to support future flood early-warning systems and intelligent environmental monitoring applications. Full article

(This article belongs to the Section Smart System Infrastructure and Applications)

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22 pages, 11832 KB

Open AccessArticle

FQDA-ML: A Hierarchical Machine Learning-Powered Data Quality Framework for Evaluating Agile Sprint Performance: A Case Study in Community Engagement Projects

by Mario Pérez-Cargua, Elizabeth Salazar-Jácome, Javier De la Torre-Guzmán, Félix Chávez-Jácome and Wilson Sánchez-Ocaña

Future Internet 2026, 18(9), 473; https://doi.org/10.3390/fi18090473 - 11 Sep 2026

Agile software development continuously generates operational data through sprint execution, task completion, and effort estimation. However, the quality of these data is rarely assessed before they are used for analytics and predictive modeling, particularly in non-industrial settings. Existing data quality approaches primarily focus [...] Read more.

Agile software development continuously generates operational data through sprint execution, task completion, and effort estimation. However, the quality of these data is rarely assessed before they are used for analytics and predictive modeling, particularly in non-industrial settings. Existing data quality approaches primarily focus on industrial big data contexts and provide limited guidance for evaluating agile sprint repositories. This study proposes FQDA-ML, a hierarchical machine learning-powered framework for assessing sprint data quality and analyzing its relationship with delivery performance. The framework adapts Cai and Zhu’s five-dimensional data quality model and operationalizes it through 13 measurable indicators aggregated into the Sprint Data Quality Index (SDQI). The framework was validated using 129 real sprints from 13 software development teams during one academic year, organized into two consecutive academic semesters (2024-S1 and 2024-S2), corresponding to two student cohorts involved in community engagement software projects. The results showed a mean SDQI of 0.603 ± 0.217 and a strong association between SDQI and sprint completion rate

(ρ = 0.719 . p < 0.001)

. The predictive evaluation achieved an AUC-ROC of 0.968 under standard five-fold cross-validation and 0.726 under Leave-One-Group-Out validation, highlighting the influence of team-level dependency on model generalization. The findings provide an empirically validated framework for data quality assessment and predictive software analytics in agile environments. Full article

(This article belongs to the Topic Data Intelligence and Computational Analytics)

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38 pages, 19248 KB

Open AccessSystematic Review

Sustainability–Resilience Trade-Offs in Edge-Enabled Systems: A Comprehensive Survey

by Nithya Nedungadi and Sriram Sankaran

Future Internet 2026, 18(9), 472; https://doi.org/10.3390/fi18090472 - 8 Sep 2026

Edge-enabled Internet of Things (IoT) systems are rapidly becoming the operational substrate of mission-critical infrastructure spanning industrial automation, smart healthcare, vehicular ecosystems, and cyber–physical environments. The distributed, resource-constrained, and physically exposed nature of these systems makes them persistent targets for a diverse and [...] Read more.

Edge-enabled Internet of Things (IoT) systems are rapidly becoming the operational substrate of mission-critical infrastructure spanning industrial automation, smart healthcare, vehicular ecosystems, and cyber–physical environments. The distributed, resource-constrained, and physically exposed nature of these systems makes them persistent targets for a diverse and evolving spectrum of cyber attacks. Critically, cyber attacks on edge-enabled IoT systems do not merely threaten data confidentiality; they simultaneously erode two interdependent operational objectives: sustainability, the ability of the system to maintain continuous, energy-efficient operation within its resource envelope and resilience, the ability to absorb adversarial disruptions, recover operational continuity, and adapt to prevent recurrence. The structural conflict between defending sustainability and maintaining resilience under active cyberattack conditions constitutes a research gap that prior surveys have not systematically addressed. This survey introduces a cyber attack-driven Sustainability–Resilience (S-R) framework that positions cyber threats as the primary stressor forcing a bilateral trade-off between operational efficiency and continuity in edge-enabled IoT systems. A five-layer, attack-centric taxonomy is developed spanning: network-layer attacks (DDoS, MitM, routing manipulation, jamming); device and firmware attacks (malware injection, firmware compromise, sensor spoofing); data and AI/ML attacks (adversarial inputs, data poisoning, model inversion); federated and Byzantine attacks (gradient poisoning, backdoor injection, free-riding); and advanced persistent threats (APT-class intrusions, ransomware, LLM prompt injection, zero-day exploitation). For each attack class, the survey systematically analyses the impact on sustainability and resilience objectives, the resulting S-R conflict, and the state-of-the-art defensive strategies. The framework is formalised as a maximin optimisation over the joint S-R objective surface, incorporating the adaptive, goal-directed nature of the adversary through a game-theoretic formulation. Cross-domain analysis spanning Industrial IoT, smart healthcare, Internet of Vehicles, UAV-assisted IoT, smart grids, and tactical edge networks establishes domain-specific S-R operating constraints under representative attack scenarios. The survey concludes with a structured characterisation of open research challenges and forward-looking directions, providing a prioritised research agenda for advancing simultaneously sustainable and adversarially resilient edge-enabled IoT ecosystems. Full article

(This article belongs to the Special Issue Security and Privacy Issues in the Internet of Cloud—2nd Edition)

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26 pages, 455 KB

Open AccessArticle

LLM-Assisted Porting of Security-Critical C Libraries to Idiomatic Rust: A Multi-Model Empirical Study

by Marco Parrillo, Marco Grassi and Luigi Laura

Future Internet 2026, 18(9), 471; https://doi.org/10.3390/fi18090471 - 7 Sep 2026

Memory-safety vulnerabilities remain the dominant class of security defects in C/C++ software underpinning Internet infrastructure. Rust offers a structural solution through its ownership system, yet migrating existing codebases remains costly. This paper defines a structured methodology for LLM-assisted porting of security-critical C libraries [...] Read more.

Memory-safety vulnerabilities remain the dominant class of security defects in C/C++ software underpinning Internet infrastructure. Rust offers a structural solution through its ownership system, yet migrating existing codebases remains costly. This paper defines a structured methodology for LLM-assisted porting of security-critical C libraries to idiomatic Rust and applies it to cJSON (∼3200 LOC, 14 CVEs). A manual expert porting serves as the baseline; five LLMs (Claude Opus 4.6, Gemini 3 Pro, GPT-5.4, Kimi K2.7-Code, and Qwen3.5-27B) produce independent portings in agentic mode. Verification uses an end-to-end pipeline: CVE-specific tests, coverage-guided and differential fuzzing (>1.7 billion executions), Miri analysis, and comparative benchmarking. All six portings eliminate all in-scope CVE classes by construction, with zero unsafe blocks and zero memory-safety crashes. In this case study, structural safety holds consistently across all five evaluated models and across all five Kimi repetitions, whereas code quality varies widely (0–9 residual bugs). Because only Kimi was repeated (

N = 5

), its variance bounds run-to-run noise at the 95% confidence level, against which some but not all between-model differences are distinguishable from chance; a single porting attempt costs approximately $3 in API usage. Differential fuzzing reveals complementary bugs in the manual and LLM portings, supporting a hybrid workflow, and we translate these findings into concrete practical guidance for teams planning a similar migration. These results are scoped to one compact, single-threaded C library. The entire codebase and evaluation pipeline are publicly released. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence and Machine Learning for Cybersecurity)

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36 pages, 639 KB

Open AccessSystematic Review

A Systematic Literature Review on Machine Learning for Intrusion Detection Systems

by Ali Ahmed, Ramy Mostafa, Mahmoud H. Qutqut and Noha Ragab

Future Internet 2026, 18(9), 470; https://doi.org/10.3390/fi18090470 - 7 Sep 2026

The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDSs), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Despite extensive research [...] Read more.

The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDSs), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Despite extensive research on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to provide a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks are the most common types in the literature, whereas Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation. Full article

(This article belongs to the Special Issue Privacy-Preserving and Secure Machine Learning)

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62 pages, 14001 KB

Open AccessArticle

Evaluation of Vanilla and RAG-Supported LLM Comprehension of the EU AI Act

by Eleni Tsalera, Gerasimos Pagiatakis and Andreas Papadakis

Future Internet 2026, 18(9), 469; https://doi.org/10.3390/fi18090469 - 4 Sep 2026

This paper presents a comparative benchmarking study evaluating the regulatory comprehension of four open-source large language models, TinyLlama-1.1B-Chat, Gemma-2B-Instruct, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.3, on the EU Artificial Intelligence Act (Regulation EU 2024/1689). A custom benchmark of 100 multiple-choice questions was constructed and classified across [...] Read more.

This paper presents a comparative benchmarking study evaluating the regulatory comprehension of four open-source large language models, TinyLlama-1.1B-Chat, Gemma-2B-Instruct, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.3, on the EU Artificial Intelligence Act (Regulation EU 2024/1689). A custom benchmark of 100 multiple-choice questions was constructed and classified across three dimensions, namely cognitive difficulty, knowledge type, and regulatory mechanism, with a balanced answer key distribution. Each model was evaluated under vanilla inference, relying exclusively on parametric knowledge, and retrieval-augmented generation (RAG), in which relevant Act passages are retrieved using a FAISS-indexed sentence embedding pipeline with standardized top three chunk retrieval applied uniformly across all models. Vanilla accuracy ranges from 22.0% for TinyLlama-1.1B to 80.0% for Llama-3.1-8B, indicating that larger models perform better. Under the retrieval configuration employed, RAG improves performance of the evaluated models, with gains from 3.0 percentage points for TinyLlama-1.1B to 17.0 for Gemma-2B and 10.0 for both 7B–8B models. Questions related to procedural knowledge, governance and enforcement emerge as weaknesses in the baseline, vanilla setting, partially mitigated by retrieval. Qualitative analysis identifies two RAG failure modes: retrieval failures, where the embedding mechanism returns informationally insufficient passages, and integration failures, where the correct passage is retrieved but not correctly exploited. The study contributes an empirical characterization of small and medium language model regulatory comprehension and a reusable 100-question benchmark. Full article

(This article belongs to the Section Cybersecurity)

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50 pages, 14774 KB

Open AccessArticle

QKD-Secured Industrial Smart-Grid Cyber-Physical Systems: Simulation and Q-MambaKAN Detection of Adaptive Side-Channel Attacks

by Ayoub Alsarhan, Bashar S. Khassawneh, Laith Alzboon, Kholoud Alkayid, Mahmoud AlJamal, Eslam Al Maghayreh, Fiyad Ahmad Alenazi and Hussein Al-Ofeishat

Future Internet 2026, 18(9), 468; https://doi.org/10.3390/fi18090468 - 3 Sep 2026

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, [...] Read more.

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021. Full article

(This article belongs to the Special Issue Cyber-Physical Systems in Industrial Communication Systems)

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35 pages, 2185 KB

Open AccessArticle

An Operational Framework for SOC-Integrated Attack Detection in 5G Standalone Networks

by Miklós Orsós and Anna Bánáti

Future Internet 2026, 18(9), 467; https://doi.org/10.3390/fi18090467 - 3 Sep 2026

The deployment of 5G standalone (SA) networks introduces cloud-native core architectures, service-based interfaces, and programmable radio access networks that substantially expand the mobile attack surface. Existing work has focused mainly on protocol-level vulnerabilities or isolated anomaly detection, with less attention to SOC-level monitoring, [...] Read more.

The deployment of 5G standalone (SA) networks introduces cloud-native core architectures, service-based interfaces, and programmable radio access networks that substantially expand the mobile attack surface. Existing work has focused mainly on protocol-level vulnerabilities or isolated anomaly detection, with less attention to SOC-level monitoring, correlation, and investigation of 5G-specific threats. This paper presents a SOC-integrated, protocol-aware detection framework for 5G SA environments, combining structured telemetry ingestion, log normalization, decision-based detection logic, and analyst-oriented visualization within an Elastic-Stack-based SOC architecture. The approach is implemented on an experimental 5G SA testbed at Obuda University and evaluated using a dual-source design: controlled testbed scenarios complemented by an observational analysis of telemetry from an independent, large-scale international cyber-defense exercise whose adversarial complexity is difficult to reproduce synthetically. The evaluation exercises attack scenarios including rogue network element registration, authentication abuse, and control- and user-plane manipulation. The results indicate that embedding 5G-aware detection logic into SOC workflows supports situational awareness and structured incident investigation. A single-layer versus cross-layer ablation of the published detection rules quantifies where cross-layer correlation is strictly required for detection versus where it primarily enriches interpretation. This is extended with a limited generic-SIEM-style baseline check and a live, a priori repeated-trial sensitivity check (

N = 30

, Wilson 95% CI [88.6%, 100%]). The framework offers a reproducible methodological foundation for operational 5G security monitoring and practical guidance for next-generation mobile network defense. Future work will extend controlled repeated-trial evaluation to the remaining detection rules, broaden the SIEM baseline comparison, and assess generalizability beyond Open5GS. Full article

(This article belongs to the Special Issue Anomaly and Intrusion Detection in Networks)

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35 pages, 8298 KB

Open AccessArticle

ZTSafe: Safety-Certified Risk-Adaptive Scheduling for Zero-Trust Time-Sensitive Industrial Networks

by Haozhe Zhou, Hang Lei and Maolin Yang

Future Internet 2026, 18(9), 466; https://doi.org/10.3390/fi18090466 - 30 Aug 2026

Zero-trust security continuously re-evaluates the trustworthiness of industrial devices and reacts by rerouting, isolating, or rescheduling traffic. In a time-sensitive network (TSN) that carries feedback control loops, however, every such reaction is itself a control-plane disturbance: a reroute that meets every deadline can [...] Read more.

Zero-trust security continuously re-evaluates the trustworthiness of industrial devices and reacts by rerouting, isolating, or rescheduling traffic. In a time-sensitive network (TSN) that carries feedback control loops, however, every such reaction is itself a control-plane disturbance: a reroute that meets every deadline can still deliver stale measurements, and an optimizer that crashes mid-reconfiguration can leave the network in an undefined state. This paper presents ZTSafe, a scheduling architecture that treats physical safety—not attack blocking—as the object of guarantee. The guarantee has two distinct layers: compliance with the communication contract yields a deterministic invariance result conditional on the stated plant, disturbance, synchronization, and trusted-base assumptions, whereas the risk bound’s

1 - δ

coverage is an empirical probabilistic calibration result. ZTSafe (i) synthesizes, offline and per control loop, a communication safety contract that bounds delay, age of information (AoI), consecutive losses, jitter, and path risk such that the physical state remains in its safe set under those assumptions; (ii) converts zero-trust evidence into conservative risk upper bounds and couples the admissible path-risk budget to the runtime safety margin of the plant; and (iii) places the scheduling optimizer outside the trusted computing base: an independent runtime shield checks every proposed schedule against the contracts, and on solver timeout, crash, or infeasibility the system atomically switches to a pre-checked fallback instead of executing an unverified approximate solution. Here, “verified” means independently checked by the shield, not machine-verified; a systematic shield defect or compromise of the remaining trusted computing base voids the deterministic claim. On a hardware TSN testbed with three physical control loops and fourteen attack and fault scenarios, ZTSafe reduces safe-set violations by 92.9% relative to the strongest baseline (12.8% to 0.9%; two-proportion

z = 39.4

,

p < 10^{- 15}

) while sustaining 94.3% on-time completion of critical traffic, recovers within three control periods, and executes zero unverified configurations across 10,000 injected solver failures. Full article

(This article belongs to the Special Issue Cybersecurity, Privacy, and Trust in Intelligent Networked Systems)

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