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Future Internet
Scopus
ISSN 1999-5903 · MDPI AG · 3.635 artikel di indeks
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Machine Learning-Driven Intrusion Detection for Securing IoT-Based Wireless Sensor Networks
Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion detection framework…
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Lightweight Authentication and Dynamic Key Generation for IMU-Based Canine Motion Recognition IoT Systems
The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks…
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Payment Rails in Smart Contract as a Service (SCaaS) Solutions from BPMN Models
The adoption of blockchain-based smart contracts for the trading of goods and services promises greater transparency, automation, and trustlessness, but also raises challenges related to payment integration and modularity. While business analysts (BAs) can express business logic and control flow…
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A Large-Scale Empirical Study of LLM Orchestration and Ensemble Strategies for Sentiment Analysis in Recommender Systems
This paper presents a comprehensive empirical evaluation comparing meta-model aggregation strategies with traditional ensemble methods and standalone models for sentiment analysis in recommender systems beyond standalone large language model (LLM) performance. We investigate whether aggregating…
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A Systematic Review of Machine-Learning-Based Detection of DDoS Attacks in Software-Defined Networks
Software-Defined Networking (SDN) has emerged as a fundamental architecture for future Internet systems by enabling centralized control, programmability, and fine-grained traffic management. However, the logical centralization of the SDN control plane also introduces critical vulnerabilities…
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Optimizing Collaborative Filtering for Accurate Rating Predictions in Very Sparse Datasets
Collaborative filtering is one of the most widely used methods for user rating prediction in recommender systems. To evaluate a collaborative filtering system, rating datasets are typically used, which comprise thousands to millions of records consisting of user–item–rating tuples. Initially, a…
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FedAWR: Aggregation Optimization in Federated Learning with Adaptive Weights and Learning Rates
Federated Learning (FL) enables collaborative model training without sharing raw data, offering a promising solution for privacy-sensitive applications. However, in real-world deployments, significant disparities in client computational capabilities lead to imbalanced model updates, resulting in…
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Assuring Brokerage Quality in the Cloud–Edge Continuum
The Cloud–Edge Continuum (CEC) has emerged as a paradigm for distributing computational resources across cloud, fog, and edge layers, enabling latency-sensitive applications to operate efficiently. However, ensuring the quality of service (QoS) brokerage in such environments remains a challenge…
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Novel Video Understanding Approach for Embodied Learning of Robotics Technology
Embodied learning involves the use of the physical embodiment of hands-on experiences, including gestures, body language, and gaze, during the instructional process for facilitation of the learning outcomes of robotics technology. Understanding the embodiment process is however challenging. In this…
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A Hybrid LoRa/ZigBee IoT Mesh Architecture for Real-Time Performance Monitoring in Orienteering Sport Competitions: A Measurement Campaign on Different Environments
The sport of orienteering requires athletes to reach specific points marked on a map (called “punching stations”) in the shortest possible time. Currently, the recording of athletes’ passages through the stations is performed offline. In addition to delays in generating intermediate and final…
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Deep Learning-Based Video Watermarking: A Robust Framework for Spatial–Temporal Embedding and Retrieval
This paper introduces a deep learning-based framework for video watermarking that achieves robust, imperceptible, and fast embedding under a wide range of visual and temporal conditions. The proposed method is organized into seven modules that collaboratively perform frame encoding, semantic region…
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Image-Based Malware Classification Using DCGAN-Augmented Data and a CNN–Transformer Hybrid Model
With the rapid growth and diversification of malware, accurate multi-class detection remains challenging due to severe class imbalance and limited labeled data. This work presents an image-based malware classification framework that converts executable binaries into 64×64 grayscale images, employs…
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Differential Privacy Data Publication Based on Scoring Function
Existing Bayesian network-based differential privacy algorithms predominantly employ uniform privacy budget allocation. However, since attribute nodes carry heterogeneous information loads, the traditional privacy budget allocation strategy may result in insufficient noise being added to important…
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A Comparative Analysis of Self-Aware Reinforcement Learning Models for Real-Time Intrusion Detection in Fog Networks
Fog computing extends cloud services to the network edge, enabling low-latency processing for Internet of Things (IoT) applications. However, this distributed approach is vulnerable to a wide range of attacks, necessitating advanced intrusion detection systems (IDSs) that operate under resource…
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Analysis of GitHub Advanced Security: Security Integration in GitHub and Azure DevOps
This paper examines the integration and operationalization of GitHub Advanced Security (GHAS) across GitHub and Azure DevOps to enhance DevSecOps practices in cloud-native software delivery. As organizations increasingly adopt continuous integration and continuous deployment (CI/CD) pipelines…
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Beyond Centralized AI: Blockchain-Enabled Decentralized Learning
The dominance of centralized artificial intelligence architectures raises significant concerns regarding privacy, data ownership, and control. These limitations have motivated the development of decentralized learning paradigms that aim to remove reliance on a central authority during model…
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Interference-Resilient Concurrent Sensing in Dense Environments: A Review of OFDM and OTFS Waveforms for JRC
This paper presents a unified perspective on Orthogonal Frequency-Division Multiplexing (OFDM)-based joint radar–communication (JRC) sensing, focusing on the efficient reuse of time and frequency resources in range–Doppler estimation and imaging scenarios. By leveraging OFDM’s inherent subcarrier…
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Evaluating Reconstruction-Based and Proximity-Based Methods: A Four-Way Comparison (AE, LSTM-AE, OCSVM, IF) in SCADA Anomaly Detection Under Inverted Imbalance
This article investigates and compares four unsupervised anomaly detection algorithms: the Autoencoder (AE), LSTM-Autoencoder (LSTM-AE), One-Class SVM (OCSVM), and the Isolation Forest (IF). The analysis focuses on SCADA telemetry data from an urban wind turbine, characterized by a unique case of…
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A Review of Research on the Applications of Large Models in Each Functional Module of the Entire Rehabilitation Process
Population ageing and chronic disease are increasing demand for rehabilitation, while resources remain limited. This review does not report an implemented end-to-end system; instead, it proposes a modular workflow framework for applying large AI foundation models across rehabilitation. Organised…
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A Temporally Dynamic Feature-Extraction Framework for Phishing Detection with LIME and SHAP Explanations
Phishing remains one of the most pervasive social engineering threats, exploiting human vulnerabilities and continuously evolving to bypass static detection mechanisms. Existing machine learning models achieve high accuracy but often act as opaque systems that lack robustness to evolving tactics…
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