Evolution of Machine Learning Applications in IoT Security: A Critical Analysis and Future Perspectives

Vaishali N. Rane, Arunkumar

2025

Abstract

The continuously advancing field of Internet of Things (IoT) has given rise to technologies that are crucial to protect IoT systems from various attacks. Conventional security methods have their own bottlenecks and fail to effectively deal with the security of IoT systems. This review explores the existing Artificial Intelligence and Machine Learning (ML) based approaches for IoT security. Covering researches mainly done in 2023-24, we have discovered that AI-enabled security solutions have better accuracy in detecting threats, more than 90%. On the other hand, they keep a check on the computational cost to prevent any cost overruns. Our key takeaways include the integration of multiple ML algorithms as a hybrid system, adversarial attack handling mechanisms and other techniques to handle targeted attacks. However, challenges persist, including resource constraints, ease of deployment, adaptability, robustness etc. Our review addresses these challenges and provide direction for future research along with the aim to offer valuable insights from relevant researches aimed at enhancing IoT security through Machine Learning Techniques.

Paper Citation

in Harvard Style

Rane V. and Arunkumar. (2025). Evolution of Machine Learning Applications in IoT Security: A Critical Analysis and Future Perspectives. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 409-414. DOI: 10.5220/0013866600004919

in Bibtex Style

@conference{icrdicct`2525,
author={Vaishali Rane and Arunkumar},
title={Evolution of Machine Learning Applications in IoT Security: A Critical Analysis and Future Perspectives},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={409-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013866600004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Evolution of Machine Learning Applications in IoT Security: A Critical Analysis and Future Perspectives
SN - 978-989-758-777-1
AU - Rane V.
AU - Arunkumar.
PY - 2025
SP - 409
EP - 414
DO - 10.5220/0013866600004919
PB - SciTePress