Real-Time Anomaly Detection in IoT Networks Using Deep Neural Models

Abstract

The rapid expansion of Internet of Things (IoT) networks has introduced unprecedented connectivity across smart cities, healthcare systems, industrial automation, and intelligent transportation. However, this widespread deployment has also increased the vulnerability of IoT infrastructures to cyberattacks, operational faults, and abnormal behaviors. Traditional anomaly detection techniques, which rely heavily on static rules or handcrafted features, struggle to adapt to the dynamic and heterogeneous nature of IoT environments. To address these challenges, this paper presents a comprehensive study on real-time anomaly detection in IoT networks using deep neural models. The proposed framework leverages deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders to identify anomalous traffic patterns with high accuracy and low latency. The methodology emphasizes real-time data acquisition, feature normalization, model training, and deployment within resource-constrained IoT environments. Extensive experimental evaluations demonstrate that deep neural models significantly outperform traditional machine learning approaches in terms of detection accuracy, false positive reduction, and scalability. The findings confirm the suitability of deep learning-based anomaly detection systems for securing next-generation IoT networks while maintaining operational efficiency.