×
The submission system is temporarily under maintenance. Please send your manuscripts to
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.
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.