Future Internet

23 pages, 2428 KB

Open AccessArticle

Heterogeneous Conditional Counter-Inspection: Configurable Error Control and Weak-Filter Recovery for 5G Network Intrusion Detection

by Khaoula Tahori, Imade Fahd Eddine Fatani, Mohamed Moughit and Hicham Magri

Future Internet 2026, 18(7), 381; https://doi.org/10.3390/fi18070381 - 22 Jul 2026

Viewed by 314

Abstract

Intrusion detection systems for 5G networks are typically reported at a single operating point, obscuring the trade-off between missed attacks and false alarms that governs real deployments. Building on a lightweight conditional counter-inspection pipeline, in which a global classifier is selectively validated by [...] Read more.

Intrusion detection systems for 5G networks are typically reported at a single operating point, obscuring the trade-off between missed attacks and false alarms that governs real deployments. Building on a lightweight conditional counter-inspection pipeline, in which a global classifier is selectively validated by curriculum-biased experts under a unanimous dissent rule, we remove the constraint that all components share one learning algorithm, assigning decision trees, random forests, extremely randomized trees, and histogram-based gradient boosting independently to the global (G), malicious-biased (EM), and benign-biased (EB) roles. Across two datasets of contrasting difficulty, 5G-NIDD and UNSW-NB15, all 14 evaluated tree-based configurations reduce missed attacks, by 36.5–79.6% on 5G-NIDD, confirming that the recovery effect is a property of the architecture rather than of decision trees. The expert assignment also selects which error the system controls: the same pipeline can be steered toward fewer false alarms, fewer missed attacks, or higher aggregate F1 without retraining the first stage. The mechanism also rescues a weak linear filter: on 5G-NIDD it cuts false positives and false negatives by 92.8% and 95.8%, and on UNSW-NB15 it raises F1 from 0.903 to 0.934 while reducing missed attacks by 35.5%. These results reframe the pipeline as a configurable validation layer matched to a deployment’s cost structure. We further show, through direct measurement on both datasets, that the conditional routing evaluates at most four of seven models per record, keeping classifier inference below 0.1 ms per record and leaving the detection stage a small contributor to overall processing cost. Full article

(This article belongs to the Special Issue Cybersecurity and Resilience in IoT and Distributed Networks (Including AI‑Enabled Approaches))

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6 pages, 208 KB

Open AccessEditorial

Artificial Intelligence and Control Systems for Industry 4.0 and 5.0: Recent Advances, Knowledge Gaps, and Future Research Directions

by Filipe Pereira and Paulo Leitão

Future Internet 2026, 18(7), 380; https://doi.org/10.3390/fi18070380 - 21 Jul 2026

Viewed by 318

Abstract

Artificial intelligence, control systems, industrial IoT, digital twins, and cyber–physical production systems are reshaping the technological foundations of advanced manufacturing [...] Full article

(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)

26 pages, 793 KB

Open AccessArticle

Imbalance-Aware Cross-Modal Focal Modulation for Cross-Dataset Audio-Visual Deepfake Detection

by Shahad Mohammad Bn Dokiey, Tariq M. Khan and Qazi Emad Ul Haq

Future Internet 2026, 18(7), 379; https://doi.org/10.3390/fi18070379 - 20 Jul 2026

Viewed by 373

Abstract

Audio-visual deepfake detection remains challenging under cross-dataset distribution shift, especially when the source-domain training data are severely imbalanced. Existing middle-fusion detectors often rely on softmax-based cross-attention, which can learn sharp source-domain token interactions and may transfer poorly to unseen datasets. This study proposes [...] Read more.

Audio-visual deepfake detection remains challenging under cross-dataset distribution shift, especially when the source-domain training data are severely imbalanced. Existing middle-fusion detectors often rely on softmax-based cross-attention, which can learn sharp source-domain token interactions and may transfer poorly to unseen datasets. This study proposes FocalNet, an audio-visual detector that replaces the cross-attention block of the 2D3MF framework with cross-modal focal modulation. The proposed module aggregates multi-scale temporal context before audio-visual interaction, enabling softmax-free contextual modulation between visual MARLIN features and audio EAT features. We evaluate the method under a strict FakeAVCeleb-to-DFDC protocol, where all training and validation is performed on FakeAVCeleb and the DFDC is used only as an unseen target-domain test set. Compared with the reproduced 2D3MF baseline, which collapses to a single-class prediction pattern on the DFDC, FocalNet achieves substantially stronger zero-shot score separation, with a DFDC ROC-AUC of 0.9324. Thresholded analysis further shows an improved balanced accuracy, macro F1 score, and MCC when the frozen source-domain operating point is applied. The model also preserves practical efficiency, requiring comparable FLOPs and lower per-sample inference time than the reproduced baseline. These findings suggest that cross-modal focal modulation is a promising alternative to attention-based middle fusion for audio-visual deepfake detection under dataset shifts, while broader validation across additional unseen datasets, multi-seed training, and deployment-oriented calibration remain important for future work. Full article

(This article belongs to the Special Issue Cybersecurity and Resilience in IoT and Distributed Networks (Including AI‑Enabled Approaches))

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25 pages, 3830 KB

Open AccessArticle

Voltage/VAR Control in Active Distribution Networks via DRL Under False Data Injection Attacks on Distributed PV Systems

by Haoyu You, Deyuan Lu, Ju Lin and Yingjun Lv

Future Internet 2026, 18(7), 378; https://doi.org/10.3390/fi18070378 - 20 Jul 2026

Viewed by 310

Abstract

Owing to its capability to handle uncertainties and provide rapid responses, Deep Reinforcement Learning (DRL) has been widely applied to Volt-Var Control (VVC) in Active Distribution Networks (ADNs). However, existing studies still present two main limitations. First, the characteristics of power equipment have [...] Read more.

Owing to its capability to handle uncertainties and provide rapid responses, Deep Reinforcement Learning (DRL) has been widely applied to Volt-Var Control (VVC) in Active Distribution Networks (ADNs). However, existing studies still present two main limitations. First, the characteristics of power equipment have not been adequately integrated with the action properties of DRL, which may compromise the control performance. Second, current DRL-based VVC methods for ADNs remain insufficiently resilient to False Data Injection Attacks (FDIAs) targeting Photovoltaic systems (PVs), significantly increasing the risks of voltage instability and operational insecurity in distribution networks. To address these challenges, a novel segmented power-constraint method is proposed to reconcile the mismatch between the control characteristics of traditional PV inverters and the action-generation mechanism of DRL agents. Furthermore, by incorporating a Distribution-Based Correction Observer into the twin delayed deep deterministic policy gradient algorithm, the proposed method enhances the resilience of DRL-based control against corrupted PV measurement data. This enables the agent to maintain reliable decision-making capabilities even when PV data are compromised. Simulation results demonstrate that the proposed method effectively enhances voltage stability, reduces power losses, and maintains robust control performance under false data injection attacks. Full article

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27 pages, 2238 KB

Open AccessArticle

Trigger, Not Root Cause: Re-Examining GitHub’s 2024 to 2026 Availability Decline Under Artificial Intelligence-Driven Load

by Vlad-Ștefan Dieaconu, Răzvan Rughiniș, Ebru Resul and Dinu Țurcanu

Future Internet 2026, 18(7), 377; https://doi.org/10.3390/fi18070377 - 20 Jul 2026

Viewed by 2066

Abstract

Over two years, the code-hosting platform GitHub suffered a sustained decline in availability that became acute by mid-2026. A widely repeated explanation blames artificial intelligence, in particular AI coding assistants and autonomous agents acting on code at machine speed. We argue that this [...] Read more.

Over two years, the code-hosting platform GitHub suffered a sustained decline in availability that became acute by mid-2026. A widely repeated explanation blames artificial intelligence, in particular AI coding assistants and autonomous agents acting on code at machine speed. We argue that this account confuses a trigger with a root cause. Treating the episode as an explanatory single-case study built only from public evidence, namely GitHub’s reports and post mortems, independent incident tracking, disclosures from other operators, and the reliability literature, we separate proximate triggers from structural causes. Three pre-existing weaknesses recur: services coupled tightly enough for a localized fault to cascade, weak protection against misbehaving client traffic, and capacity that could not expand quickly enough to absorb non-diurnal load. Agentic traffic exposed and amplified these weaknesses but did not create them; GitHub’s own statements and the theory of metastable failure support this reading, and incidents with the same structural signature predate the surge by more than a year. We set out architectural remedies, among them cell-based isolation with shuffle sharding, decoupling of critical paths, admission control and load shedding, quality-of-service tiering, and predictive elasticity, and close with a resilience agenda for an internet where automated requests now exceed human ones. Full article

(This article belongs to the Special Issue Disruptive Technologies and Digital Transformation)

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52 pages, 8425 KB

Open AccessArticle

PromptSentinel-X: A Leakage-Aware and Context-Aware Framework for Prompt-Injection Detection in Large Language Model-Powered Web Agents

by Lily Popova Zhuhadar

Future Internet 2026, 18(7), 376; https://doi.org/10.3390/fi18070376 - 19 Jul 2026

Viewed by 513

Abstract

Large language model (LLM)-powered web agents combine privileged instructions with user requests, webpages, retrieved documents, tool outputs, memory, and conversation history, creating prompt-injection risks that static text classification may not capture. This study presents PromptSentinel-X, a leakage-aware and context-aware screening framework. The primary [...] Read more.

Large language model (LLM)-powered web agents combine privileged instructions with user requests, webpages, retrieved documents, tool outputs, memory, and conversation history, creating prompt-injection risks that static text classification may not capture. This study presents PromptSentinel-X, a leakage-aware and context-aware screening framework. The primary benchmark contained 1581 English records from the Prompt Injection Malignant dataset; 30,015 external prompt-injection, jailbreak, and benign hard-negative records were reserved for transfer and stress testing. The framework uses prompt-family-aware partitioning, trusted–untrusted context segmentation, calibrated risk prediction, robustness analysis, and deployment-oriented routing. On a 465-record group-aware test set, PromptSentinel-X achieved 0.9849 accuracy, 0.8887 macro-F1, 0.9851 attack recall, a 0.0050 benign false-positive rate, 0.9971 area under the receiver operating characteristic curve (AUROC), 0.9836 area under the precision-recall curve (AUPRC), and 0.0153 expected calibration error. Random splitting produced a higher baseline macro-F1 but 144 leakage warnings. Context-aware macro-F1 declined from 0.8301 for static prompts to 0.6306 for multi-turn, 0.3844 for retrieval-augmented generation, and 0.3200 for memory scenarios. PromptSentinel-X is positioned as a calibrated screening and escalation component, not a stand-alone authorization mechanism. Broader tool, memory, multimodal browser, multilingual, and end-to-end agent studies remain necessary. Full article

(This article belongs to the Special Issue Advances in Agentic and Generative AI for Secure Software Systems and Cyber-Resilience in Future Internet)

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19 pages, 481 KB

Open AccessArticle

Enhancing Orthopedic Care with Telemedicine: Assessing Feasibility and Patient Engagement in Early Discharge Pathways

by Daniela Platano, Roberto Tedeschi, Leonardo Pellicciari, Stefania Orsini, Antonella Orlandi Magli, Giuseppina Mariagrazia Farella, Federico Vender, Lisa Berti and Fabio La Porta

Future Internet 2026, 18(7), 375; https://doi.org/10.3390/fi18070375 - 19 Jul 2026

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Abstract

Background: To investigate the feasibility of telemedicine-enabled functional assessments using a patient-reported outcome questionnaire based on the International Classification of Functioning Disability (ICF) and Health in elderly subjects following a program of early home discharge for femur fracture. Design: A questionnaire consisting of [...] Read more.

Background: To investigate the feasibility of telemedicine-enabled functional assessments using a patient-reported outcome questionnaire based on the International Classification of Functioning Disability (ICF) and Health in elderly subjects following a program of early home discharge for femur fracture. Design: A questionnaire consisting of 59 items associated with the appropriate ICF categories and including the condition of functioning, activity and participation, and relevant contextual factors was developed to define the patient function profile one month after hospital discharge. Subjects/Patients: Elderly patients who underwent surgery for femur fracture were recruited from an orthopedic unit of the hospital. Methods: The questionnaire, together with an assessment of patients’ satisfaction, was administered through a telemedicine platform. Results: Only 75.9% (22 subjects, age = 79 ± 9.7 years, 73% female) of the recruited participants completed the questionnaire within the designated timeframe. Walking impairments and difficulty in climbing stairs were reported as the most affected activities. Regarding the patients’ satisfaction, most of the patients were satisfied with the proposed tele-evaluation, although 73% were against further remote evaluation. Conclusions: The findings emphasize the challenges of elderly patients’ adherence to tele-evaluation, highlighting difficulties in the use of new technologies within specific patient cohorts. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled Smart Healthcare)

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30 pages, 7036 KB

Open AccessArticle

VizARE: An Intermediate Representation to Support the Visualization of Association Rules in Data Mining

by Carlos Fernandez-Basso, Maria Dolores Ruiz, Miguel Molina-Solana and Maria J. Martin-Bautista

Future Internet 2026, 18(7), 374; https://doi.org/10.3390/fi18070374 - 17 Jul 2026

Viewed by 256

Abstract

Data mining techniques are currently highly useful and widely used in industry, business and government. However, their broad adoption is sometimes limited because non-expert users are required to accurately interpret and deal with the complex results obtained. In this paper, we put forward [...] Read more.

Data mining techniques are currently highly useful and widely used in industry, business and government. However, their broad adoption is sometimes limited because non-expert users are required to accurately interpret and deal with the complex results obtained. In this paper, we put forward a methodology for the display of association rules using an intermediate form. This technique enables efficient processing of the rules by generating a standard format through a graph structure that allows us to adapt the rules to different display tools. We also show some illustrative examples of the usefulness of this intermediate form. Full article

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28 pages, 8578 KB

Open AccessArticle

A Lightweight Fingerprint Recognition Pipeline Based on Hierarchical Energy-Feature Decomposition

by Yajuan Sun, Maolin Li, Qinge Wu and Shuyan Wu

Future Internet 2026, 18(7), 373; https://doi.org/10.3390/fi18070373 - 17 Jul 2026

Viewed by 263

Abstract

Fingerprint recognition remains challenging when ridge structures are degraded by noise, weak contrast, translation, rotation, and local deformation during acquisition. Although deep-learning approaches have improved biometric recognition, they often require large labeled datasets and carefully specified training protocols, which can limit their use [...] Read more.

Fingerprint recognition remains challenging when ridge structures are degraded by noise, weak contrast, translation, rotation, and local deformation during acquisition. Although deep-learning approaches have improved biometric recognition, they often require large labeled datasets and carefully specified training protocols, which can limit their use in small-data or resource-constrained scenarios. This paper presents a lightweight fingerprint recognition pipeline based on hierarchical energy-feature decomposition. The pipeline integrates Template Integrated Mean (TIM) preprocessing, region-of-interest localization, coefficient-feature extraction, energy-feature extraction, and two-stage template matching. Coefficient features are used for coarse candidate screening, whereas energy features are used for fine matching within the reduced candidate set. On the evaluated fingerprint dataset, the proposed method achieves a closed-set identification accuracy of 97.86% under the reported gallery/probe protocol. Additional aggregate-level statistical checks and baseline configuration details are provided to clarify the evaluation scope and comparison protocol. Full article

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30 pages, 1108 KB

Open AccessArticle

Secure V2I Authentication and Handover Protocol Leveraging Blockchain and Physically Unclonable Functions

by Xiang Gong, Zhaoming Xu and Tao Feng

Future Internet 2026, 18(7), 372; https://doi.org/10.3390/fi18070372 - 17 Jul 2026

Viewed by 278

Abstract

With the rapid development of Vehicular Ad Hoc Networks (VANETs), Vehicle-to-Infrastructure (V2I) communication plays a critical role in Intelligent Transportation Systems (ITS). However, existing authentication and key exchange protocols face challenges such as high computational cost, large communication overhead, and security and privacy [...] Read more.

With the rapid development of Vehicular Ad Hoc Networks (VANETs), Vehicle-to-Infrastructure (V2I) communication plays a critical role in Intelligent Transportation Systems (ITS). However, existing authentication and key exchange protocols face challenges such as high computational cost, large communication overhead, and security and privacy risks in high-speed mobile environments. To address these problems, this paper proposes a lightweight V2I authentication key exchange and ticket-based fast handover authentication protocol based on consortium blockchain and a Physical Unclonable Function (PUF). The proposed framework integrates PUF-based device binding, biometric-assisted user binding, PRF-based dynamic pseudonym update, target-RSU-bound handover tickets, and consortium blockchain-assisted auditability. To avoid privacy leakage on immutable ledgers, the blockchain stores only keyed pseudonym indexes, cryptographic commitments, timestamps, revocation states, and audit records, whereas biometric helper information, PUF-derived values, long-term secrets, handover keys, and session keys are protected in TPM/HSM or encrypted off-chain storage. Formal verification using ProVerif indicates that the revised protocol satisfies the modeled secrecy properties, injective mutual authentication for initial authentication and handover, and non-injective ticket origin authenticity for accepted handover tickets. In addition, the Real-or-Random (RoR) model is used to prove fresh session key indistinguishability under explicit pre- and post-Test freshness, PUF unpredictability, fuzzy extractor security, and hardware-protected secret assumptions. Analytical performance evaluation further shows the core cryptographic cost of the proposed scheme while explicitly separating and parameterizing deployment-dependent TPM/HSM, AEAD, blockchain lookup, and PBFT confirmation costs. Full article

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23 pages, 262 KB

Open AccessArticle

Empirical Evaluation of a DevSecOps Proxy Pipeline for Multi-Tier Web Applications

by Abderrahim Rida, Abdelaziz Bakhil and Ayoub Ait Lahcen

Future Internet 2026, 18(7), 371; https://doi.org/10.3390/fi18070371 - 17 Jul 2026

Viewed by 273

Abstract

The use of DevSecOps seeks to ensure that the functionality of securing software is incorporated throughout the software development life cycle, but the difference between the theoretical model and the empirically proven version, specifically for multi-tier web applications, remains wide. This research proposes [...] Read more.

The use of DevSecOps seeks to ensure that the functionality of securing software is incorporated throughout the software development life cycle, but the difference between the theoretical model and the empirically proven version, specifically for multi-tier web applications, remains wide. This research proposes the evaluation of a “proxy” DevSecOps pipeline, defined as an automated intermediary architecture that decouples intensive security scanning from the primary build flow to prevent bottlenecks. Designed specifically for multi-tier PHP-based web applications, the model leverages infrastructure as code (Terraform) and Static Application Security Testing (SAST) to ensure that security validation remains continuous without impeding the integration speed. This research utilized the Terraform model on the Amazon Web Services cloud platform, with three EC2 instances and Jenkins integration, and the use of many tools for the testing process, divided into the following experiments for DevSecOps functionality measurement: the first for the baseline efficiency process, the dynamic scaling process, the validation test for the fail-safe mechanism, and the accuracy process for the vulnerabilities’ detection. This research indicates that the process exhibited stability and consistency for the average execution times, which took 122.08 ± 1.69 s, and low values for the 5.04% additional cost for concurrent executions. The 100% activation process for the fail-safe mechanism for the injection of vulnerabilities indicates that the process took 41.82% of the total pipeline execution time, demonstrating that security validation is the most time-intensive part of the automated proxy workflow, and the low costs of 0.0047 dollars for the entire process, specifically for the infrastructure aspect. The false positives for the process were measured as 10–15%, and the low costs for the entire process, which took 0.56–0.58 s for the total process. Full article

(This article belongs to the Special Issue Cloud Computing and Cloud Service Orchestration)

31 pages, 9442 KB

Open AccessArticle

Risk-Aware TimeMixer with Asymmetric Upper-Bound Calibration for Cloud CPU Utilization Forecasting

by Xiaoqi Jin and Xiaolan Xie

Future Internet 2026, 18(7), 370; https://doi.org/10.3390/fi18070370 - 16 Jul 2026

Viewed by 311

Abstract

Cloud central processing unit (CPU) utilization forecasting is fundamental to capacity planning, overload warning, elastic scaling, and resource provisioning in cloud computing systems. Conventional forecasting models usually optimize average point-error accuracy, whereas provisioning decisions are often more sensitive to high-load underestimation and upper-bound [...] Read more.

Cloud central processing unit (CPU) utilization forecasting is fundamental to capacity planning, overload warning, elastic scaling, and resource provisioning in cloud computing systems. Conventional forecasting models usually optimize average point-error accuracy, whereas provisioning decisions are often more sensitive to high-load underestimation and upper-bound failures that indicate potential under-provisioning risk. This paper proposes Risk-Aware TimeMixer (RA-TimeMixer), a provisioning-oriented adaptation of Original TimeMixer for machine-level multi-step CPU utilization forecasting. RA-TimeMixer preserves the multiscale forecasting backbone and introduces two targeted risk-oriented components: batch-wise high-load weighted training and residual-based asymmetric upper-bound calibration. Experiments are conducted on a preprocessing-audited 50-machine subset of Alibaba Cluster Trace 2018 with 1 min sampling, input length 96, and prediction lengths 6, 12, and 24. At prediction length 12, RA-TimeMixer reduces High-load MAE, Under-rate high, Under-magnitude high, and Under-MAE when under by 2.72%, 1.89%, 4.06%, and 1.98%, respectively, compared with Original TimeMixer. Machine-level paired analyses, horizon and threshold studies, three-seed stability, persistence-baseline diagnostics, and fully observed-window retraining support the robustness of the observed accuracy–risk trade-off. The results indicate that RA-TimeMixer offers a transparent, risk-sensitive extension of TimeMixer for provisioning-oriented cloud CPU forecasting, while asymmetric calibration reduces empirical upper-bound violations at the cost of wider intervals and margins. Full article

(This article belongs to the Special Issue Cloud Computing and Cloud Service Orchestration)

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23 pages, 541 KB

Open AccessArticle

Joint Element and Power Optimization in NOMA-RIS-Assisted Indoor Near-Field Communications

by Periyakarupan Gurusamy Sivabalan Velmurugan, Vinoth Babu Kumaravelu, Samikkannu Rajkumar, Arthi Murugadass, Mathan Nanjan Suresh and Samarendra Nath Sur

Future Internet 2026, 18(7), 369; https://doi.org/10.3390/fi18070369 - 16 Jul 2026

Viewed by 282

Abstract

Reconfigurable intelligent surfaces (RIS) equipped with extremely large aperture arrays (ELAA) are emerging as a key technology for enhancing beamforming gain, spatial multiplexing, and angular resolution in sixth-generation (6G) wireless networks. When combined with non-orthogonal multiple access (NOMA), RIS can further improve spectral [...] Read more.

Reconfigurable intelligent surfaces (RIS) equipped with extremely large aperture arrays (ELAA) are emerging as a key technology for enhancing beamforming gain, spatial multiplexing, and angular resolution in sixth-generation (6G) wireless networks. When combined with non-orthogonal multiple access (NOMA), RIS can further improve spectral efficiency, system throughput, and energy efficiency. However, most existing studies on RIS-aided NOMA assume far-field propagation, where the incident wavefronts are approximately planar. In contrast, RIS-ELAA systems operating at millimeter wave (mmWave) experience spherical wavefronts in the radiative near-field regions. Also, it creates spatial non-stationarity and distance-dependent phase curvature. These effects invalidate the conventional monotonic path-loss assumption and make fairness-oriented NOMA design more challenging. This paper proposes a joint element and power optimization (JEPO) algorithm for near-field RIS-ELAA-assisted indoor NOMA systems, in which the RIS is dynamically partitioned into user-specific subarrays performing near-field phase synthesis toward the near user (NU) and far user (FU). The reversed far-to-near successive interference cancellation (SIC) ordering, governed by an effective FU channel gain greater than an effective NU channel gain, is formally established, and a closed-form optimal power allocation is derived by reducing the max-min fairness condition to a scalar quadratic in the target signal-to-interference-plus-noise ratio (SINR), eliminating iterative power search. Simulation results confirm that JEPO consistently outperforms four baseline schemes across transmit power, NU distance, angular separation, and RIS aperture size, with the largest gain observed at

θ_{NU} \approx 40^{\circ}

where fixed-partition baselines collapse to near-zero fairness while JEPO maintains robust performance. Full article

(This article belongs to the Special Issue Disruptive Technologies and Digital Transformation)

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1 pages, 126 KB

Open AccessRetraction

RETRACTED: Zhang et al. MCS-VD: Alliance Chain-Driven Multi-Cloud Storage and Verifiable Deletion Scheme for Smart Grid Data. Future Internet 2026, 18, 56

by Lihua Zhang, Jiali Luo, Yi Yang and Wenbiao Wang

Future Internet 2026, 18(7), 368; https://doi.org/10.3390/fi18070368 - 16 Jul 2026

Viewed by 221

Abstract

The journal retracts the article titled “MCS-VD: Alliance Chain-Driven Multi-Cloud Storage and Verifiable Deletion Scheme for Smart Grid Data” [...] Full article

(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT—3rd Edition)

26 pages, 1913 KB

Open AccessArticle

EPoLBFT: A Blockchain Consensus Algorithm for Enhancing Privacy, Invulnerability and Trust in IoT System

by Yunus Kareem, Djamel Djenouri and Essam Ghadafi

Future Internet 2026, 18(7), 367; https://doi.org/10.3390/fi18070367 - 15 Jul 2026

Viewed by 372

Abstract

The rapid growth of Internet of Things (IoT) systems has introduced significant challenges related to privacy, trust, scalability, and attack resilience, particularly in resource-constrained and location-sensitive environments. Existing blockchain consensus mechanisms provide decentralised trust, but they often suffer from high communication overhead, weak [...] Read more.

The rapid growth of Internet of Things (IoT) systems has introduced significant challenges related to privacy, trust, scalability, and attack resilience, particularly in resource-constrained and location-sensitive environments. Existing blockchain consensus mechanisms provide decentralised trust, but they often suffer from high communication overhead, weak physical-context awareness, and limited privacy protection when deployed in large-scale IoT networks. This paper proposes Elastic Proof-of-Location Byzantine Fault Tolerance (EPoLBFT), a privacy-preserving and location-aware blockchain consensus framework for IoT systems. The proposed design enables IoT nodes to prove regional eligibility without revealing exact coordinates while restricting consensus participation to trusted and geographically verified validators. EPoLBFT is evaluated using the Blockchain IoT Consensus Algorithm (BICA) simulator under normal, high-load, Byzantine, Sybil, location-spoofing, and denial-of-service scenarios. The evaluation considers latency, throughput, communication overhead, energy consumption, and attack resilience. The results show that EPoLBFT reduces communication overhead and improves consensus efficiency compared with conventional PBFT-based approaches while strengthening resilience against location-based and identity-based attacks. The study also discusses the privacy–latency trade-off introduced by zk-PoL, the assumptions related to trusted location anchors, and the limitations of simulation-based evaluation. Full article

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

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42 pages, 9339 KB

Open AccessArticle

Toward Low-Delay and Energy-Efficient UAV-Assisted MEC Systems Through Intelligent Resource Allocation

by Walaa Saber, Hanan Algamil, Fifi Farouk and Asmaa Mohamed

Future Internet 2026, 18(7), 366; https://doi.org/10.3390/fi18070366 - 15 Jul 2026

Viewed by 239

Abstract

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has emerged as a promising paradigm for supporting computation-intensive and delay-sensitive applications. However, efficient task offloading and resource allocation remain a challenging problem due to the need to jointly minimize the maximum processing delay and [...] Read more.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has emerged as a promising paradigm for supporting computation-intensive and delay-sensitive applications. However, efficient task offloading and resource allocation remain a challenging problem due to the need to jointly minimize the maximum processing delay and energy consumption of User Device (UD) in dynamic environments. Existing solutions often suffer from training instability, limited exploration capabilities, and slow convergence, limiting their ability to achieve optimal task offloading and resource allocation decisions. To address these challenges, this paper proposes a Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory. The proposed algorithm introduces a state-aware normalization mechanism to stabilize the learning process, a new pre-training initialization technique that populates the Experience Replay Buffer (ERB) before learning to accelerate convergence and improve policy quality, and a hybrid noise exploration scheme that enhances exploration efficiency. Furthermore, to achieve an effective balance between delay and energy consumption, a novel adaptive weighting mechanism based on a modified Exponential Moving Average (EMA) algorithm is proposed. Simulation results demonstrate that the proposed PAW-DDPG algorithm outperforms DDPG and all baseline algorithms, achieving performance gains over DDPG of 4–18%, 9–18%, 8–26%, and 7–18% under varying task sizes, UAV computing capabilities, user device computing capabilities, and numbers of user devices, respectively. Full article

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21 pages, 4242 KB

Open AccessArticle

Knowledge Graphs vs. SQL over Structured EHR Data

by Leonidas Anagnou, Andreas Vezakis, Ioannis Vezakis, Ioannis Kakkos, Ourania Petropoulou and George K. Matsopoulos

Future Internet 2026, 18(7), 365; https://doi.org/10.3390/fi18070365 - 15 Jul 2026

Viewed by 381

Abstract

Clinical question answering over electronic health records (EHRs) increasingly relies on large language model (LLM) agents that retrieve structured patient data through external tools. Published benchmarks, however, evaluate these systems at a single patient-population size, and rarely measure the effect of backend representation [...] Read more.

Clinical question answering over electronic health records (EHRs) increasingly relies on large language model (LLM) agents that retrieve structured patient data through external tools. Published benchmarks, however, evaluate these systems at a single patient-population size, and rarely measure the effect of backend representation from that of the retrieval interface design. This paper compares six retrieval configurations that vary along two axes: backend (a property graph database, a relational database and a dense vector index) and interface design (curated domain-specific tool calls, model-generated queries, full-text search, and single-shot dense retrieval). The evaluation covers a 334-question bank spanning six categories (simple lookup, multi-hop, temporal, cohort, reasoning, and unanswerable), instantiated at three nested population scales: 200, 2000, and 20,000 alive patients from a single Synthea cohort. Four models are compared: Claude Haiku 4.5, Qwen 2.5 72B, Llama 3.1 8B, and Llama 3.3 70B, spanning closed-frontier and open-source alternatives. Curated tool-calling configurations improve accuracy over retrieval-augmented baselines for capable models, but reduce accuracy for a small open-source model due to function-calling protocol failures. We report how accuracy, latency, and cost evolve with each approach, model size, and cohort size, supported by paired statistical tests and confidence intervals. All benchmark components, databases, and evaluation code are publicly available. Full article

(This article belongs to the Topic AI Agents: Progress, Architecture, and Applications)

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24 pages, 4531 KB

Open AccessArticle

Towards an Accessible Industry 4.0: Design and Experimental Validation of a Reproducible IIoT Architecture Based on a Compact PLC Platform, Factory I/O, Node-RED and Azure

by Félix Chávez-Jácome, Jhonatan Guagalango-Minga, Elizabeth Salazar-Jácome and Javier De la Torre-Guzmán

Future Internet 2026, 18(7), 364; https://doi.org/10.3390/fi18070364 - 15 Jul 2026

Viewed by 642

Abstract

This paper presents the design and experimental validation of a hybrid architecture for the Industrial Internet of Things (IIoT), based on a Siemens LOGO! v8.4 programmable logic controller (PLC) (Siemens AG, Munich, Germany), Node-RED v3.1.3 (OpenJS Foundation, Wilmington, DE, USA), Microsoft Azure (Microsoft [...] Read more.

This paper presents the design and experimental validation of a hybrid architecture for the Industrial Internet of Things (IIoT), based on a Siemens LOGO! v8.4 programmable logic controller (PLC) (Siemens AG, Munich, Germany), Node-RED v3.1.3 (OpenJS Foundation, Wilmington, DE, USA), Microsoft Azure (Microsoft Corporation, Redmond, WA, USA), and Factory I/O v2.5.5 (Real Games Unipessoal Lda, Gondomar, Porto, Portugal). The proposal integrates three main functions—real-time telemetry, structured historical data storage and bidirectional remote control—over an automated tank level process. The architecture was organized into four functional layers: local automation, middleware integration, cloud services and web application. Validation was conducted using five performance metrics: end-to-end latency, remote command latency, successful delivery rate, update rate, and service recovery time. The results confirm the technical feasibility of the proposed architecture for remote monitoring and control, while also identifying the main bottlenecks in the transmission chain. Overall, the study provides an experimental basis for modular and reproducible IIoT solutions with a low adoption barrier and potential applicability to progressive digitalization scenarios in small- and medium-sized enterprises. Full article

(This article belongs to the Special Issue Intelligent Industrial IoT)

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21 pages, 567 KB

Open AccessArticle

Generalised Potential Game-Based Resource Allocation in SDN-Enabled O-RAN Systems

by Evangelos D. Spyrou, Chrysostomos Stylios, Vassilios Kappatos and Constantinos T. Angelis

Future Internet 2026, 18(7), 363; https://doi.org/10.3390/fi18070363 - 15 Jul 2026

Viewed by 246

Abstract

The evolution of wireless networks toward 6G and Open Radio Access Network (O-RAN) architectures brings unprecedented demands for flexible and energy-efficient resource allocation mechanisms. A key challenge is to allocate radio resources effectively among heterogeneous units while satisfying diverse quality-of-service (QoS) requirements. Traditional [...] Read more.

The evolution of wireless networks toward 6G and Open Radio Access Network (O-RAN) architectures brings unprecedented demands for flexible and energy-efficient resource allocation mechanisms. A key challenge is to allocate radio resources effectively among heterogeneous units while satisfying diverse quality-of-service (QoS) requirements. Traditional allocation methods often fail to capture energy efficiency considerations or lack adaptability in highly dynamic and decentralized environments. To address this, we formulate the resource allocation problem as a non-cooperative game among SDN-enabled Central Units (CUs) and Distributed Units (DUs), where each player’s utility captures a trade-off between throughput gains and resource costs under threshold-based SINR QoS constraints. We show that the game admits an exact generalized potential function, guaranteeing the existence of a pure-strategy Nash equilibrium and convergence under sequential best response dynamics. The SDN controller supervises the network by adjusting system-level parameters, such as the resource price, to guide the network toward efficient and fair allocations. This formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks. The proposed framework is evaluated against both classical resource allocation strategies (equal and greedy allocation) and advanced optimization-based and game-theoretic baselines, including convex optimization, proportional fairness, water-filling, and Stackelberg formulations, and shows competitive performance. Full article

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

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39 pages, 1627 KB

Open AccessReview

A Survey of LSTM Pedestrian Intention Prediction and Lightweight Methods for Intelligent Guide Sticks

by Yijia Cai, Fang Jing, Huafeng Qu, Yuxi Xie and Shafrida Sahrani

Future Internet 2026, 18(7), 362; https://doi.org/10.3390/fi18070362 - 15 Jul 2026

Viewed by 363

Abstract

The travel problem of visually impaired people is a worldwide issue that needs urgent attention. Although intelligent guide sticks provide obstacle detection and early warning through multi-sensor fusion and embedded algorithms, existing systems generally cannot model the temporal movement patterns of dynamic obstacles, [...] Read more.

The travel problem of visually impaired people is a worldwide issue that needs urgent attention. Although intelligent guide sticks provide obstacle detection and early warning through multi-sensor fusion and embedded algorithms, existing systems generally cannot model the temporal movement patterns of dynamic obstacles, such as pedestrians and vehicles, thereby hindering intention prediction and active obstacle avoidance. Long short-term memory (LSTM), with its gating mechanism, effectively captures long-term dependencies in trajectories and offers a promising solution. This review compares and analyzes LSTM against other mainstream temporal models under the resource constraints of intelligent guide sticks and finds that LSTM demonstrates a favorable combination in temporal modeling capability, lightweight maturity, and edge deployment feasibility. We categorize five lightweight techniques—architecture simplification, low-rank decomposition, structured pruning, quantization, and knowledge distillation—and examine their compression effectiveness, accuracy preservation, and hardware applicability across typical platforms. Furthermore, this review surveys application cases in speech guidance, trajectory prediction-based obstacle avoidance, positioning and navigation, edge computing, and Internet collaboration, exploring the diverse potential of LSTM in intelligent guide stick scenarios. The findings indicate that, after lightweight processing, LSTM models can meet the deployment requirements of resource-constrained edge devices, suggesting their potential feasibility on resource-constrained hardware platforms. However, existing applications still face challenges in balancing real-time performance and accuracy, meeting stringent resource constraints, and the absence of end-to-end validation on real intelligent guide stick prototypes. The reviewed evidence suggests that LSTM-based prediction represents a promising and practically valuable pathway for transitioning intelligent guide sticks from passive response to active prediction. Future research should prioritize real-world deployment validation, domain-specific data collection, and hardware-software co-design to realize its potential fully. Full article

(This article belongs to the Special Issue Distributed Intelligence for IoT and Smart Systems)

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33 pages, 2073 KB

Open AccessArticle

A Stateless PIR Protocol for Root-Aligned Wildcard Prefix-Set Retrieval Based on Compact Trie and Homomorphic Encryption

by Xinhui Cui, Tengyang Wang and Zhiqiang He

Future Internet 2026, 18(7), 361; https://doi.org/10.3390/fi18070361 - 14 Jul 2026

Viewed by 318

Abstract

Private information retrieval (PIR) enables a client to retrieve records from a server-hosted database without revealing the requested item. Most high-performance single-server PIR systems are optimized for exact index or keyword lookup, whereas controlled-vocabulary and structured-identifier applications may require a partially specified root-aligned [...] Read more.

Private information retrieval (PIR) enables a client to retrieve records from a server-hosted database without revealing the requested item. Most high-performance single-server PIR systems are optimized for exact index or keyword lookup, whereas controlled-vocabulary and structured-identifier applications may require a partially specified root-aligned prefix with per-position wildcards. This paper presents a stateless protocol for that restricted but practically relevant retrieval model. The construction combines a compact trie with leveled Brakerski–Gentry–Vaikuntanathan (BGV) homomorphic encryption, ciphertext–plaintext equality testing, single-instruction multiple-data (SIMD) packing, compressed-edge batching, and power-of-two slot rotations. The client stores no database-dependent hint, and the server stores no persistent client-specific evaluation material; all the query ciphertexts and required evaluation keys are uploaded in the online phase. We explicitly position the construction as a protocol-level integration for richer private retrieval semantics rather than as a new foundational PIR or homomorphic-encryption primitive. Security is formulated for an honest-but-curious single-query adversary under an explicit public leakage function covering trie topology, compressed-edge lengths, payload layout, query-upload shape, and the fixed response schedule. We further analyze broad wildcard queries: the match cardinality can reach the number of indexed keys, while the number of response ciphertexts is determined by public output capacity rather than by the private query. Experiments on three controlled synthetic datasets and one anonymized enterprise dataset show that compact trie compression and packed edge evaluation reduce server-side online latency relative to uncompressed and serial homomorphic baselines, with the largest gains on high-prefix-sharing workloads. The implementation achieves exact set-level agreement with a plaintext oracle at two wildcard densities while exposing an explicit trade-off among richer query semantics, stateless deployment, public structural leakage, and communication overhead. Full article

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25 pages, 3083 KB

Open AccessArticle

Mission-Driven UAV Conceptual Design Using LLM and RAG with Preliminary CFD and Closed-Loop Feasibility Assessment

by Sami Mahfoudhi, Mutiq Almutiq and Kamel Barkaoui

Future Internet 2026, 18(7), 360; https://doi.org/10.3390/fi18070360 - 14 Jul 2026

Viewed by 324

Abstract

Future IoT, smart-city, and 6G ecosystems will increasingly use UAVs as autonomous nodes, creating demand for rapid yet traceable mission-to-airframe design workflows. This study evaluates whether a large language model (LLM) with retrieval-augmented generation (RAG) can convert a cargo-UAV mission brief into source-grounded [...] Read more.

Future IoT, smart-city, and 6G ecosystems will increasingly use UAVs as autonomous nodes, creating demand for rapid yet traceable mission-to-airframe design workflows. This study evaluates whether a large language model (LLM) with retrieval-augmented generation (RAG) can convert a cargo-UAV mission brief into source-grounded preliminary sweep-angle ranges and whether one instantiated geometry remains compatible with downstream physics-based checks. An LLM first proposed broad ranges; a RAG stage, over a curated aerospace corpus, narrowed the three sweep intervals from a combined width of 25° to 7° (72% aggregate reduction) before CAD instantiation. The resulting tailless blended-wing geometry was screened with a SolidWorks 2025 Flow Simulation database spanning speed, angle of attack, sideslip, and elevon deflections. In the neutral-elevon, near-zero-sideslip slice, lift crossed the CAD-derived weight between 4° and 8° at 100 m/s and between 0° and 4° at 200 m/s. A three-level mesh check preserved force and moment signs. A nominal 6-DoF mission at 120 m/s over approximately 32 km remained within the prescribed corridor, with late mean cross-track and altitude-path errors of 10.7 m and 7 m. The contribution is a traceable early-stage design workflow, not a claim of aerodynamic optimality, flight readiness, controller robustness, or certification-level validation. Full article

(This article belongs to the Special Issue Distributed Intelligence for IoT and Smart Systems)

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22 pages, 2352 KB

Open AccessArticle

Phase-Adaptive Model Routing in LLM-Driven SSH Honeypots: Balancing Response Fidelity and Latency Across the Attack Lifecycle

by Raiymbek Magazov, Fatima Uralova, Kuanysh Abeshev, Guldana Akhmedi and Gulnur Aksholak

Future Internet 2026, 18(7), 359; https://doi.org/10.3390/fi18070359 - 14 Jul 2026

Viewed by 372

Abstract

Secure Shell (SSH) intrusions against Linux servers remain a dominant vector of opportunistic and targeted cyber incidents, yet operational honeypots treat attacker commands either as isolated lookup keys for static templates or as input to a single uniform language model. Two research directions [...] Read more.

Secure Shell (SSH) intrusions against Linux servers remain a dominant vector of opportunistic and targeted cyber incidents, yet operational honeypots treat attacker commands either as isolated lookup keys for static templates or as input to a single uniform language model. Two research directions partially address this gap: LLM-driven response generation raises interaction realism but incurs a fidelity–latency trade-off, while semantic command analytics classifies attack stages from embeddings yet applies a single fine-tuned model uniformly across sessions. This work introduces the phase-adaptive model routing framework (PAMR), which explicitly couples both views. A lightweight online phase estimator infers the current attack stage—reconnaissance, exploitation, or persistence—from a sliding window of recent commands via compact semantic embeddings; a router then dispatches each command to one of several heterogeneous LLM backends according to estimated phase, command complexity, and a confidence-weighted latency budget. The routing decision is formulated as a constrained optimization problem and realized at runtime as an

O (1)

lookup in a precomputed dispatch table. PAMR is evaluated on a controlled, reproducible benchmark of 412 stage-annotated command sessions that combines representative Linux command–response pairs with synthesized attacker traces; we explicitly state that this is a laboratory benchmark rather than live attacker traffic, and we scope our claims accordingly. Relative to uniform-model baselines, PAMR reduces mean response latency by approximately 38% against an API-hosted high-capacity backend and by approximately 45% against a locally hosted mid-capacity backend, while keeping token-level response-fidelity metrics close to the high-capacity baseline (cosine similarity ≈ 0.39 vs. 0.40) and maintaining an online stage-classification macro-F1 above 0.87. We further provide a first-order analytical treatment of the timing side-channel that any backend-routing architecture introduces, and we frame the contribution as a latency/token-fidelity trade-off, leaving validation of operational realism against live adversaries to future work. Full article

(This article belongs to the Special Issue Advances in Agentic and Generative AI for Secure Software Systems and Cyber-Resilience in Future Internet)

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24 pages, 1322 KB

Open AccessArticle

Multi-Version Managers for Large Scalable Data-Management Systems

by Baya Chalabi and Yahya Slimani

Future Internet 2026, 18(7), 358; https://doi.org/10.3390/fi18070358 - 13 Jul 2026

Viewed by 208

Abstract

With the emergence of data-intensive computing, which is due to the growth of the data produced and generated each day, it became necessary to store and manage big data. Cloud data storage is actually the best choice for large distributed systems. Successful Cloud [...] Read more.

With the emergence of data-intensive computing, which is due to the growth of the data produced and generated each day, it became necessary to store and manage big data. Cloud data storage is actually the best choice for large distributed systems. Successful Cloud Computing cannot be achieved without a reliable data-management system to store and handle the enormous volume of data. Management of the available storage system at large scale becomes progressively more complicated, and we face many challenges, such as scalability, data availability, fault tolerance, etc. Also, data storage is faced with specific access patterns: highly concurrent reads of data from the same file, many overwrites, and very concurrent appends to the same file. Most of the existing storage systems use versioning to bring and enhance data access parallelism and this enables better performance levels under concurrency; but, generally, these systems use one component (version manager), which is responsible for generating new versions of each file stored. When we speak in the context of big data, the requests for read, write and append increase. If these requests are managed by a single component, then we have a performance bottleneck and an overloaded version manager. To avoid this drawback, we proposed and designed a new architecture of storage systems that uses versioning; the new architecture uses multi-version managers to support better the scalability and provide partial fault tolerance. To illustrate the practicability of our approach, we assessed it on the BlobSeer data-management system. The experimental results demonstrate that our architecture achieves near-linear scalability for CREATE operations (495 ops/s per additional version manager), reduces WRITE execution time by up to 66%, and maintains 67% availability under single-node failures, all while introducing minimal resource overhead (3% aggregate CPU increase). These results confirm that the proposed multi-version manager architecture offers a practical, scalable, and partially fault-tolerant solution for Cloud data-storage systems. Full article

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36 pages, 7879 KB

Open AccessArticle

A-MCAC: An Adaptive Mobility- and Connectivity-Aware Clustering Framework for Stable and Efficient Internet of Vehicles Communications

by Khalid Kandali, Khalid Balar and Hamid Bennis

Future Internet 2026, 18(7), 357; https://doi.org/10.3390/fi18070357 - 10 Jul 2026

Viewed by 365

Abstract

The Internet of Vehicles (IoV) enables intelligent transportation services through real-time communication between vehicles and infrastructure. However, high mobility and frequent topology changes often reduce cluster stability and communication reliability. Traditional clustering approaches commonly rely on static decision criteria and periodic maintenance mechanisms, [...] Read more.

The Internet of Vehicles (IoV) enables intelligent transportation services through real-time communication between vehicles and infrastructure. However, high mobility and frequent topology changes often reduce cluster stability and communication reliability. Traditional clustering approaches commonly rely on static decision criteria and periodic maintenance mechanisms, leading to frequent reclustering operations and increased communication overhead. This paper proposes an Adaptive Mobility- and Connectivity-Aware Clustering (A-MCAC) framework for IoV environments. The proposed approach combines predictive mobility analysis, link lifetime estimation, adaptive weight optimization, and event-driven cluster maintenance to improve Cluster Head (CH) selection and cluster stability. By dynamically adapting clustering decisions according to network conditions, A-MCAC aims to reduce reclustering operations and enhance communication efficiency. The proposed framework is evaluated through simulations and compared with CARAC, MetaLearn, and CPB using cluster lifetime, CH lifetime, reclustering rate, link stability, clustering overhead, packet delivery ratio, end-to-end delay, and throughput. Results demonstrate that A-MCAC improves cluster stability, increases communication reliability, maintains a favorable overhead-performance trade-off, and achieves better network performance across varying vehicle densities. These findings demonstrate that A-MCAC improves both clustering stability and communication efficiency in highly dynamic IoV environments. Full article

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22 pages, 492 KB

Open AccessArticle

TransForge: A Genetic Algorithm Framework for Cross-Category Evaluation of Endpoint Detection Robustness to Code Transformations

by Alvina Rwaichi Minja and Jema David Ndibwile

Future Internet 2026, 18(7), 356; https://doi.org/10.3390/fi18070356 - 9 Jul 2026

Viewed by 438

Abstract

Endpoint protection systems increasingly rely on a combination of signature-based and behavioral detection mechanisms, yet their robustness under systematic code transformation remains insufficiently understood. This paper presents a multi-category evaluation of endpoint detection robustness under automated, semantic-preserving code transformations across diverse execution variants. [...] Read more.

Endpoint protection systems increasingly rely on a combination of signature-based and behavioral detection mechanisms, yet their robustness under systematic code transformation remains insufficiently understood. This paper presents a multi-category evaluation of endpoint detection robustness under automated, semantic-preserving code transformations across diverse execution variants. We introduce TransForge, a generalized transformation framework designed to generate functionally equivalent execution variants for controlled robustness assessment across heterogeneous artifact categories and programming environments. Building on our prior work, ShellForge, which focused on a single artifact class, TransForge extends this approach to support multi-category analysis through a modular transformation pipeline and an evolutionary strategy that enables non-deterministic variant generation. Using a dataset of 75 base samples spanning six execution categories and four programming languages, we conduct controlled experiments to evaluate how endpoint detection systems respond to systematically generated variants under consistent conditions. Across the evaluated corpus, 53 of 75 evolved variants (70.7%) achieved zero VirusTotal detections across 76 engines, while 94% of variants preserved full functional correctness. Detection outcomes varied substantially by category: staged downloaders exhibited a mean of 8.75 alerts while credential access, surveillance, and cross-site execution variants produced zero alerts under identical evolutionary configuration, with all six categories converging uniformly at generation 4. Statistical analysis using ANOVA, Mann–Whitney U, and Wilcoxon signed-rank tests confirm that these differences are statistically significant (

p < 0.001

) and practically meaningful (Cohen’s

d > 0.8

). The findings highlight coverage gaps in both signature-based and behavioral detection pipelines when faced with semantic-preserving transformations, motivating the development of robustness-aware evaluation frameworks and detection pipelines that leverage behavioral correlation and adaptive analysis beyond static signature matching. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

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20 pages, 578 KB

Open AccessArticle

Opinion Dynamics in Social Networks with Edge-Heterogeneous Confidence Bounds: Clustering, Polarization, and Implications for Online Platforms

by Zumei Huang, Zhuangzhuang Ma, Lei Shi and Lulu Chen

Future Internet 2026, 18(7), 355; https://doi.org/10.3390/fi18070355 - 9 Jul 2026

Viewed by 343

Abstract

Opinion polarization, echo chambers, and the rapid formation of opinion clusters have become defining features of debates on contemporary online social platforms. To explain these phenomena from a control-theoretic perspective, this paper investigates opinion dynamics in social networks with edge-heterogeneous confidence bounds, focusing [...] Read more.

Opinion polarization, echo chambers, and the rapid formation of opinion clusters have become defining features of debates on contemporary online social platforms. To explain these phenomena from a control-theoretic perspective, this paper investigates opinion dynamics in social networks with edge-heterogeneous confidence bounds, focusing on clustering and polarization behaviors driven by pair-dependent trust and asymmetric influence. Two discrete-time models are proposed, including an unsigned bounded-confidence model and a more general signed model that incorporates both supportive and oppositional interactions. The interaction structures are described by time-varying unsigned and signed digraphs, respectively, in which heterogeneous interpersonal influence is characterized by edge-dependent confidence bounds that naturally encode platform-mediated trust. For the proposed models, rigorous sufficient conditions are established for invariant cluster consensus and structurally balanced polarization. Numerical simulations, including a case study on the Slashdot Zoo signed social network with 50 controversial users, illustrate the theoretical results and demonstrate their relevance for understanding opinion evolution on internet-scale platforms. Full article

(This article belongs to the Topic The Synthetic Society: Processes and Products)

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24 pages, 2804 KB

Open AccessArticle

A Hybrid Approach to the Automatic Detection of Personal Data in Latvian-Language Texts

by Henrihs Gorskis, Jūlija Strebko, Jurijs Korņijenko, Vitālijs Zabiņako and Andrejs Romanovs

Future Internet 2026, 18(7), 354; https://doi.org/10.3390/fi18070354 - 9 Jul 2026

Viewed by 316

Abstract

In the modern world, hybrid and fully remote work formats are becoming increasingly widespread. The volume of digital communication continues to grow, and the need to exchange documents and information through email, corporate messengers (such as Microsoft Teams), and collaborative workspaces is rising. [...] Read more.

In the modern world, hybrid and fully remote work formats are becoming increasingly widespread. The volume of digital communication continues to grow, and the need to exchange documents and information through email, corporate messengers (such as Microsoft Teams), and collaborative workspaces is rising. Personal data is often involved in these exchanges, which increases the risk of unintentional disclosure. To comply with GDPR requirements and ensure information security, it is necessary to implement methods for the automatic detection of personal data. This task is particularly relevant for low-resource languages, such as Latvian, for which existing tools often operate with limited accuracy and efficiency. This work proposes a hybrid approach to the automatic detection of personal data in Latvian texts from Microsoft Teams messages, emails, and documents, combining a transformer-based NER model with rule-based detection of structured identifiers. The approach builds on a multilingual NER model, supplements it with Latvian-specific rules and structured-identifier detectors, and demonstrates the potential of adapted solutions to improve the accuracy and robustness of personal-data detection in real-world workflows. Full article

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3 pages, 131 KB

Open AccessEditorial

Generative Artificial Intelligence (AI) for Cybersecurity

by Li Yang and Raha Moraffah

Future Internet 2026, 18(7), 353; https://doi.org/10.3390/fi18070353 - 9 Jul 2026

Viewed by 362

Abstract

Generative artificial intelligence (AI), including large language models (LLMs), generative adversarial networks, autoencoders, and related generative learning methods, is increasingly shaping cybersecurity research and practice [...] Full article

(This article belongs to the Special Issue Generative Artificial Intelligence (AI) for Cybersecurity)

52 pages, 769 KB

Open AccessReview

Decentralized AI Agents and Blockchain: Architectures, Coordination Mechanisms, and Governance Frameworks

by Marios Touloupou and Evgenia Kapassa

Future Internet 2026, 18(7), 352; https://doi.org/10.3390/fi18070352 - 6 Jul 2026

Viewed by 1376

Abstract

Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination [...] Read more.

Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value. Full article

(This article belongs to the Special Issue New Trends for Blockchain Technologies)

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Future Internet, Volume 18, Issue 7 (July 2026) – 49 articles

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