Artificial Intelligence-Enabled Frameworks for Intelligent Anomaly Detection in The Internet of Things: A Comprehensive Study of Models, Challenges, And Future Trends

Abstract

The Internet of Things (IoT) has emerged as one of the most transformative technologies, connecting billions of devices across healthcare, manufacturing, transportation, and smart cities. However, the rapid expansion of IoT systems introduces significant security and reliability challenges, especially due to the high volume of heterogeneous data. Anomaly detection, a critical component in securing IoT ecosystems, aims to identify unusual patterns that may indicate faults, attacks, or system failures. Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) and Deep Learning (DL), have demonstrated exceptional potential in automating and enhancing the accuracy of anomaly detection processes. This paper provides a comprehensive study of AI-driven anomaly detection in IoT systems, exploring existing models, methodologies, datasets, and tools. It also discusses challenges such as data imbalance, scalability, privacy concerns, and interpretability, while highlighting the future research scope towards explainable, federated, and lightweight AI models for IoT anomaly detection.

KEYWORDS: Artificial Intelligence, Internet of Things, Anomaly Detection, Machine Learning, Deep Learning, Edge Computing, Cybersecurity, Data Analytics