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Go to Editorial ManagerThis research introduces a deep learning-based framework for anomaly detection in wireless communication networks using Channel State Information (CSI)—a fine-grained physical-layer signal that captures wireless channel dynamics. Traditional detection methods often fall short in identifying subtle or evolving threats, whereas CSI provides a rich, underutilized source for context-aware monitoring. Inspired by its use in human activity recognition, we apply and compare deep learning architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), and Transformers to learn normal network behavior and detect anomalies, including spoofing, jamming, rogue access points, environmental disruptions, and Quality of Service (QoS) degradation. The system supports supervised, semi-supervised, and unsupervised settings, accommodating scenarios with limited labeled data. CSI data is collected using tools like the Intel 5300 NIC and Nexmon CSI under both controlled and realistic conditions. We benchmark our models against traditional techniques (e.g., Isolation Forests, Support Vector Machines (SVMs), Principal Component Analysis (PCA)), evaluating accuracy, false positives, latency, and robustness. To enhance transparency, we employ interpretability methods such as Gradient-weighted Class Activation Mapping (Grad-CAM) and t-distributed Stochastic Neighbor Embedding (t-SNE). Experimental results show that deep learning models outperform classical baselines by up to 30% in detection accuracy. The Transformer architecture achieved 96.2% accuracy with a false positive rate of 3.9%, while the CNN-LSTM hybrid achieved the best latency–performance tradeoff (5.1ms inference). Compared to Isolation Forest and One-Class SVM, our framework reduced false positives by over 10–14%.
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.
As 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.
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.
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.