A Novel Hybrid Deep Learning Model for Real-Time Anomaly Detection in IoT Networks

Abstract

With the rapid growth of IoT ecosystems, ensuring network security has become increasingly critical. This paper proposes a hybrid deep learning framework combining convolutional neural networks (CNN) and long short-term memory (LSTM) architectures for real-time anomaly detection. The model captures both spatial and temporal patterns in network traffic data, improving detection accuracy and reducing false positives. Experimental results demonstrate superior performance compared to traditional machine learning approaches, making the proposed model suitable for scalable and secure IoT environments.