Deep Learning-Based Intrusion Detection in IoT: A Comprehensive Review of Architectures, Attacks, Challenges, and Future Directions
TL;DR
In a comparative review of forty peer-reviewed studies, it is demonstrated that hybrid DL models provide excellent detection performance (99-100% classification accuracy on benchmark datasets) as well as practical viability for deployment with privacy-preserving Federated Learning for large-scale data.
Abstract
The rapid proliferation of Internet of Things (IoT) devices across critical domains including healthcare, smart cities, industrial control systems, and intelligent transportation has fundamentally transformed the cybersecurity threat landscape. The inherent characteristics of IoT environments, namely resource-constrained devices, heterogeneous architectures, and large-scale deployment, render traditional Intrusion Detection Systems (IDS) inadequate for the sophisticated and evolving attack vectors targeting these networks. Deep learning (DL) has emerged as a compelling paradigm for next-generation IoT IDS, offering automated feature extraction, temporal pattern recognition, and adaptive threat detection capabilities that address the limitations of conventional approaches. This paper provides a thorough and systematic review of the existing DL methods for IoT intrusion detection. The paper explore the IoT architectural paradigms, outline a four layered taxonomy for types of IoT attacks across its three primary layers Perception, Network and Application as well as Adversarial Machine Learning attacks, and systematically review seven classes of DL architectures Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), Autoencoders, Generative Adversarial Networks (GAN), models based on Transformer architecture and Federated Learning frameworks. In a comparative review of forty peer-reviewed studies, we demonstrate that hybrid DL models provide excellent detection performance (99-100% classification accuracy on benchmark datasets) as well as practical viability for deployment with privacy-preserving Federated Learning for large-scale data. The study additionally highlights five enduring challenges class imbalance, adversarial vulnerability, zero-day detection limitations, computational constraints and the absence of standardized benchmarking protocols that together account for the gap between performance benchmarks and real-world deployment efficacy. It outlines future research avenues targeting on five key axes with a particular focus in the integration of Explainable AI (XAI), lightweight edge-deployable architectures, and adversarial robustness mechanisms. This survey identifies a structured reference to advance the state of IoT intrusion detection from research to operationally viable and deployable systems.