A Comprehensive Review of Deep Learning Techniques for Anomaly Detection in <scp>IoT</scp> Networks: Methods, Challenges, and Datasets
Roya Morshedi, S. Mojtaba Matinkhah
Abstract
ABSTRACT With the rapid growth of the Internet of Things (IoT) and the widespread deployment of smart connected devices, ensuring the security of these networks has become a critical challenge. Anomaly detection is considered one of the most effective techniques for identifying abnormal behaviors and cyber‐attacks in IoT networks. In recent years, deep learning techniques have gained significant attention in this domain due to their powerful capabilities in automatic feature extraction and modeling complex patterns. This review article provides a comprehensive overview of deep learning methods applied to anomaly detection in IoT networks. Various deep architectures including CNNs, LSTMs, autoencoders, GANs, and hybrid models are analyzed and compared. In addition, commonly used datasets such as CICIDS2017, BoT‐IoT, NSL‐KDD, and TON_IoT are introduced and evaluated in terms of their quality and suitability for deep learning‐based models. Key challenges including the lack of real‐world data, high resource consumption, vulnerability to adversarial attacks, and lack of interpretability are also discussed. Finally, potential future research directions are suggested to enhance the performance and real‐world applicability of deep learning‐based anomaly detection systems in IoT environments.