Deep Learning for Internet of Things Security: A Comprehensive Review of Threat Detection and Intelligent Defense

Abstract

The rapid expansion of Internet of Things (IoT) networks has introduced significant security challenges due to the large number of heterogeneous, resource-constrained, and interconnected devices. Traditional security mechanisms often struggle to identify sophisticated and rapidly evolving cyber threats in highly dynamic IoT environments. Deep Learning (DL) has emerged as a promising approach for intelligent intrusion detection, anomaly identification, malware classification, and network traffic analysis. This review examines the application of deep learning techniques to IoT cybersecurity, covering convolutional neural networks, recurrent neural networks, autoencoders, deep belief networks, and transformer-based architectures. The review analyzes how these models are employed to detect distributed denial-of-service attacks, botnets, malware, unauthorized access, and abnormal device behavior. Furthermore, the study investigates the integration of deep learning with edge computing to enable low-latency and distributed security analysis. Key challenges, including limited computational resources, insufficient training data, adversarial attacks, privacy concerns, model interpretability, and the generalization of detection models across heterogeneous IoT environments, are discussed. Emerging approaches such as federated learning, explainable AI, lightweight deep learning, and zero-trust architectures are evaluated as potential directions for strengthening IoT security. The review concludes by highlighting research gaps and opportunities for developing adaptive, privacy-preserving, and autonomous IoT security frameworks.