Abstract
The significant advancements in sensors and other resource-constrained devices, capable of collecting data and communicating wirelessly, are poised to revolutionize numerous industries through the Internet of Things (IoT). Sectors such as healthcare, energy, education, transportation, manufacturing, military, and agriculture stand to benefit. IoT is expected to play a crucial role in implementing both Industry 4.0 and its successor, Industry 5.0. IoT relies on data collected by sensors from various points, shared over wireless or wired networks, making it more vulnerable to attacks. Consequently, addressing privacy and security concerns is of paramount importance for the widespread adoption of IoT across industries. Recognizing the pivotal role of IoT security, recent years have witnessed a marked upswing in publications dedicated to leveraging Machine Learning techniques for intrusion detection within the IoT framework. This paper embarks on a comprehensive endeavor to classify and characterize the myriad of intrusion detection methodologies that have emerged through the fusion of Machine Learning and IoT security. Serving as a timely and insightful review, this survey is not only of immense value to seasoned researchers immersed in this dynamic field but also serves as an invaluable resource for newcomers eager to contribute to the enhancement of IoT security. This paper sets itself apart from existing surveys by placing particular emphasis on recent advancements in machine learning-based intrusion detection across various IoT domains. Unlike previous surveys, it comprehensively explores papers published within the past five years, encompassing a wide range of dimensions within this field. These dimensions include, but are not limited to, medical IoT, agricultural IoT, industrial IoT, Fog/Edge IoT, Intelligent Transportation Systems, Smart Home Networks, and more. By meticulously outlining the diverse machine learning-based intrusion detection methods found in the literature, this survey not only captures the current landscape but also provides a roadmap for future research endeavors. This roadmap aims to strengthen the security framework of the rapidly expanding IoT ecosystem.
| Original language | English |
|---|---|
| Article number | 103925 |
| Journal | Journal of Network and Computer Applications |
| Volume | 229 |
| DOIs | |
| State | Published - Sep 2024 |
Bibliographical note
Publisher Copyright:© 2024 Elsevier Ltd
Funding
Dr. Manivannan served as an Associate Editor of IEEE Transactions on Parallel and Distributed Systems, IEEE Communications Magazine and Wireless Personal Communications journal. Currently, he is on the Editorial Board of Information Sciences journal, Internet of Things Journal and Network journal. He served as Program co-chair of three International Conferences in the areas of reliable distributed systems and wireless networks and served as program committee member for over 60 International Conferences. He served as reviewer for more than 40 International Journals published by ACM, IEEE, Elsevier, Springer, Oxford University Press, Taylor and Francis and others. He also served on several proposal review panels of US National Science Foundation and as external tenure reviewer for other Universities. Dr. Manivannan\u2019s research has been funded by grants from the US National Science Foundation and the US Department of Treasury.
| Funders | Funder number |
|---|---|
| National Science Foundation Arctic Social Science Program | |
| US Department of Treasury |
Keywords
- Cybersecurity
- Data imbalance problem
- Deep learning
- Internet of Things
- Intrusion detection
- Machine learning
- Over-sampling
- Synthetic data generation
- Under-sampling
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications