Adaptive Deep Learning Models for Real-Time Anomaly Detection in IoT Networks

Abstract

The exponential growth of Internet of Things (IoT) networks has introduced unprecedented volumes of data and new avenues for cyber threats and operational anomalies. Traditional anomaly detection techniques struggle to meet the dynamic, heterogeneous, and resource-constrained nature of IoT environments. This paper explores the potential of adaptive deep learning models in providing real-time anomaly detection within IoT systems. By leveraging architectures such as Autoencoders, LSTM networks, and Convolutional Neural Networks, these models can learn complex data patterns and adapt to evolving threats without requiring frequent human intervention. The study discusses key implementation challenges, including computational constraints, latency requirements, and data privacy concerns. It also highlights practical applications in domains such as healthcare, industrial automation, and smart energy grids, where adaptive models have demonstrated significant value. Future directions are explored, focusing on federated learning, edge computing, and explainable AI to enhance scalability and trust. The findings underscore the promise of adaptive deep learning as a cornerstone for securing and optimizing real-time IoT ecosystems.