What does this research mean for the field?

Securing growing IoT environments requires scalable and adaptive intrusion detection systems that integrate emerging paradigms like Federated Learning and LLMs while addressing critical challenges in dataset realism, adversarial robustness, and scalability. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to survey various intrusion detection system techniques applicable to IoT environments.

May 30, 2026Open Access

Intrusion Detection in the Internet of Things: A Comprehensive Review of Techniques, Architectures, Datasets, and Emerging Trends

Key Points

The aim is to survey various intrusion detection system techniques applicable to IoT environments.
Systematic review of literature from 2021 to early 2026
Categorization of IDS techniques by detection strategy, learning paradigm, deployment architecture, and evaluation methodology
Analysis of conventional and emerging detection approaches such as machine learning and deep learning.
Identification of critical challenges including dataset realism and adversarial robustness.
Evaluation of modern techniques like Federated Learning and Explainable AI for IoT applications.
Recommendations for designing trustworthy and scalable IDS frameworks for future IoT networks.

Abstract

As the Internet of Things (IoT) grows, strong, scalable, and adaptive intrusion detection systems (IDS) become increasingly critical for protecting IoT environments. This paper presents a comprehensive and systematic survey of IDS techniques for IoT environments, covering literature from 2021 to early 2026. The review introduces a multidimensional taxonomy that categorizes IDS approaches by detection strategy, learning paradigm, deployment architecture, and evaluation methodology. We examine conventional techniques, such as signature-based and anomaly-based detection, as well as modern machine-learning and deep-learning approaches. Furthermore, emerging paradigms, including Federated Learning, Explainable AI (XAI), TinyML, Large Language Models (LLMs), Transformer, Quantum Machine Learning, Generative Adversarial Networks and Incremental Learning, are analyzed with respect to their applicability to resource-constrained IoT environments. The paper also provides a detailed analysis of publicly available IDS datasets, validation protocols, and evaluation metrics used for benchmarking detection systems. In addition, critical challenges, including dataset realism, adversarial robustness, scalability, privacy preservation, and ethical considerations, are discussed. Finally, we highlight open research directions and propose guidelines for designing next-generation, trustworthy, and scalable IDS frameworks for IoT networks.

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