A Systematic Review of Intrusion and Anomaly Detection Techniques in IoT

The growing adoption of Internet of Things, IoT, devices has introduced significant security challenges, necessitating robust intrusion detection systems tailored to resource-constrained, heterogeneous environments. This survey provides a comprehensive review of contemporary intrusion and anomaly detection techniques for IoT systems. We systematically examine three core pillars: first, feature engineering approaches, from classical statistical methods to advanced selection algorithms, second, machine learning paradigms, including supervised, unsupervised, and ensemble methods; and third, advanced deep learning architectures such as convolutional neural networks, recurrent neural networks, autoencoders, and recent Transformer-based large language models. Furthermore, we address critical deployment challenges including resource constraints, adversarial attacks, dataset limitations, and privacy preservation via federated learning. By synthesizing findings from key studies published primarily between ۲۰۲۰ and ۲۰۲۶, while acknowledging foundational literature, this paper offers researchers a structured taxonomy of current techniques and identifies open research gaps toward secure, scalable IoT intrusion detection.

کلیدواژه ها:

Internet of Things ، intrusion detection ، anomaly detection ، feature engineering ، machine learning ، deep learning