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Go to Editorial ManagerAs Internet of Things (IoT) devices continue to spread, they also create many new entry points for cyberattacks. Traditional security methods struggle to keep up, which makes smarter and more adaptive defenses necessary. This paper introduces an Artificial Intelligence (AI)–driven threat intelligence framework designed to improve intrusion detection in diverse IoT networks. The framework combines Machine Learning (ML) and Deep Learning (DL) models to detect malicious activity more accurately across different types of network traffic. To evaluate the approach, three widely used benchmark datasets—UNSW-NB15, CIC-IDS2017, and IoT-Botnet—were used. Experimental results show that the proposed hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model performs very well. It achieved 97% accuracy, a 0.95 F1-score, and a 0.98 Receiver Operating Characteristic – Area Under the Curve (ROC-AUC) on the UNSW-NB15 dataset, outperforming traditional ML models such as Random Forest, which reached 94% accuracy. While DL models provided better detection performance and stronger generalization, ML models proved to be much faster, with nearly three times lower inference latency—about 3 milliseconds per network flow. This makes them more suitable for real-time deployment at the IoT edge, where computing resources are limited. Overall, the proposed hybrid approach strikes a practical balance between detection accuracy and processing speed, offering a scalable and robust foundation for AI-based IoT threat intelligence in real-world environments.
The integration of Deep Learning (DL) techniques with the Internet of Things (IoT) has emerged as a transformative paradigm in the advancement of smart healthcare systems. Numerous recent studies have investigated the convergence of these technologies, demonstrating their potential in improving healthcare delivery, patient monitoring, and clinical decision-making. The ongoing evolution of Industry 5.0 in parallel with the deployment of 5G communication networks has further facilitated the development of intelligent, cost-effective, and highly responsive sensors. These innovations enable continuous and real-time monitoring of patients’ health conditions, a capability that was not feasible within the constraints of traditional healthcare models. Smart health monitoring systems have thus introduced significant improvements in terms of speed, affordability, reliability, and accessibility of medical services, particularly in remote or underserved regions. Moreover, the application of Deep Learning and Machine Learning algorithms in health data analysis has played a pivotal role in achieving preventive healthcare, reducing mortality risks, and enabling personalized treatment strategies. Such methods have also enhanced the early detection of chronic diseases, which previously posed considerable diagnostic challenges. To further optimize scalability and cost-efficiency, cloud computing and distributed storage solutions have been incorporated, ensuring secure and real-time data availability. This review therefore provides a comprehensive perspective on smart healthcare innovations, emphasizing the role of intelligent systems, recent advancements, and persisting challenges in the domain of digital health monitoring.
The combination of the Internet of Things (IoT) and Software-Defined Networking (SDN) has quickly revolutionized the world of industry, and also brought with it some severe security risks. Despite its recent success, Deep learning (DL) can be a powerful method for Network Intrusion Detection systems (NIDS), but often the computational and memory requirements are too high for edge devices. In this paper, a collaborative optimization framework with lightweight deep-learning IDS models suitable for SDN-IoT environments is proposed. We formalize and test three collaborative compression schemes based on combining pruning, clustering, and quantization, yet maintaining the structural advantages of each stage: Sparsity-Preserving Quantization-Aware Training (PQAT), Cluster-Preserving Quantization-Aware Training (CQAT), and Sparsity-and-Cluster-Preserving Quantization-Aware Training (PCQAT). The three schemes are evaluated on four canonical architectures (LSTM, RNN, DNN and CNN) on the proposed SICA dataset with respect to DDoS, Probe and Brute-Force (BFA).The results show that aggressive single-stage compression such as clustering can shrink model size by up to 94%, but at the cost of predictive confidence; PCQAT, by contrast, acts as a stabilizer that recovers this lost confidence. In particular, CNN-PCQAT and DNN-PCQAT deliver the most favorable trade-off, sustaining high detection accuracy on all three attack classes with inference latency below 0.01 ms. The framework therefore offers a practical, deployment-oriented guideline for implementing real-time IDS on resource-constrained SDN-IoT edge devices.
Predictive maintenance (PdM) is a key enabler of reliability and cost reduction in Industrial Internet of Things (IIoT) systems. However, conventional centralized learning approaches require raw data aggregation, raising serious concerns regarding data privacy, communication overhead, and single points of failure. Federated Learning (FL) addresses part of this challenge by enabling distributed model training, yet centralized FL architectures still rely on a central aggregator. This paper proposes a decentralized federated learning (DFL) framework based on a ring topology for multi-class predictive maintenance in IIoT environments. The proposed approach enables collaborative model training among distributed edge nodes without relying on a central server, thereby enhancing system robustness, scalability, and data privacy. A lightweight deep neural network (DNN) is employed to classify five machine operating states using multi-sensor data, including vibration, temperature, and current measurements. Extensive experiments are conducted using a real-world sensor dataset collected from three-phase AC motors under multiple fault conditions. The performance of the proposed DFL approach is systematically compared against a non-federated baseline and a centralized FL (CFL) scheme. Experimental results demonstrate that the decentralized framework achieves clas-sification performance comparable to centralized FL, with an accuracy of up to 99.83%, while eliminating the dependency on a central coordinator. The findings confirm the feasibility of decentralized federated learning for predictive maintenance applica-tions, particularly in distributed industrial settings where data privacy, fault tolerance, and communication efficiency are critical.
Keeping IoT networks running reliably in harsh environments is still a tough problem. Sensors wear out, communication links are unreliable, and maintenance quickly becomes expensive. These issues make traditional monitoring approaches fragile and slow to react. This work presents a self-adaptive, AI-driven Digital Twin framework that continuously tracks the real state of an IoT network and flags failures before they actually happen. The system mirrors the physical network in real time by combining edge-level data preprocessing, physics-aware Digital Twin simulations, and well-chosen deep learning models for anomaly detection and remaining useful life estimation. To test the idea, we simulated a network of 50 IoT nodes operating under realistic harsh conditions, including thermal stress, high humidity, and signal interference. The results are hard to ignore. The proposed framework reached 91% prediction accuracy, detected problems 27 seconds earlier on average, and improved overall network reliability from 84% to 96% compared to standard threshold-based monitoring. The takeaway is straightforward: pairing AI analytics with Digital Twin technology enables proactive and resilient IoT operation in environments where conventional monitoring quickly falls apart. This work lays a practical foundation for deploying AI-enhanced Digital Twins in real-world, next-generation IoT systems, where reliability actually matters.
This narrative study provides an analytical and critical review of recent advancements (2019-2026) in the integration of IoT (Internet of Things) and AI (Artificial Intelligence) systems for fall detection and child health monitoring. Unlike prior studies, which concentrated on elderly care and monitoring, this study examines child-specific monitoring environments, including wearable, vision-based, and hybrid systems. It investigates new trends such as the combination of deep learning and interpretable AI with multimedia sensory input and peripheral or fuzzy computing. Data scarcity, real-world deployment limits, privacy concerns, and age-related changes are among the key challenges addressed. The paper identifies important research gaps and proposes future paths for sustainable, secure, and accessible intelligent child monitoring systems.
The growing sophistication of cyber threats exposes the limits of signature-based detection in Security Information and Event Management (SIEM) systems. User and Entity Behavior Analytics (UEBA) advances SIEM by enabling behavior-based anomaly detection, yet legacy approaches struggle with high false positives and poor adaptability to evolving threats. This research proposes an AI-driven UEBA framework that combines deep learning for modeling user behavior with graph-based tools to map system relationships, enhancing anomaly detection in enterprise environments. Using datasets such as CERT Insider Threat, UNSW-NB15, and TON_IoT, we simulate diverse behaviors and evaluate performance. Our Transformer-GNN ensemble achieved an F1-score of 0.90, reduced false positives by 40%, and cut incident triage time by 78% compared to rule-based SIEM. To support real-world use, we provide an open-source pipeline integrating with SIEM platforms via Kafka, Elastic search, and a modular ML inference layer. This work bridges AI research and deployable cybersecurity practice, advancing the development of adaptive, intelligent, and robust UEBA systems.