A survey on Hybrid learning-based Anomaly Detection in IoT networks

DOI

10.2991/978-94-6239-616-6_97How to use a DOI?

Keywords

Internet of Things (IoT); IoT Security; Anomaly Detection; Intrusion Detection; Predictive Modeling; Deep Learning; Blockchain Security; Federated Learning; Edge Computing; Zero-Day Attacks

Abstract

IoT networks face increasing risks from zero-day attacks, scalability issues, and limited resources. These challenges reduce the effectiveness of traditional intrusion detection systems. Predictive modeling has come forward as a promising approach to address these problems. Sixteen recent studies published between 2021 and 2025 have been reviewed and grouped into five categories: deep learning-based intrusion detection, hybrid predictive models, blockchain-enabled frameworks, federated and edge learning, and semantic or active learning methods. Each category is analyzed based on methodology, datasets, detection performance, and limitations. The survey points out significant improvements in detection accuracy, flexibility, and transparency while also identifying ongoing challenges like energy efficiency, resilience against new threats, and large-scale deployment. A structured taxonomy and comparative analysis bring together these diverse efforts. The findings provide a valuable reference for researchers and practitioners aiming to build secure, scalable, and smart IoT anomaly detection systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Lakshmi Prabha
AU  - R. Mohamed Ibrahim
AU  - K. Nithesh Kumar
AU  - M. Arun Kumar
PY  - 2026
DA  - 2026/03/31
TI  - A survey on Hybrid learning-based Anomaly Detection in IoT networks
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1320
EP  - 1335
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_97
DO  - 10.2991/978-94-6239-616-6_97
ID  - Prabha2026
ER  -