Abstract
Although the Internet of Things (IoT) devices simplify and automate everyday tasks, they also introduce a tremendous amount of security flaws. The current insufficient security measures for smart device protection make IoT devices a potential victim of breaking into a secure infrastructure. This research proposes an on-the-fly intrusion detection system (IDS) that applies machine learning (ML) to detect network-based cyber-attacks on IoT networks. A lightweight ML model is trained on network traffic to defer benign packets from normal ones. The goal is to demonstrate that lightweight machine learning models such as decision trees (in contrast with deep neural networks) are applicable for intrusion detection achieving high accuracy. As this model is lightweight, it could be easily employed in IoT networks to classify packets on-the-fly, after training and evaluation. We compare our lightweight model with a more complex one and demonstrate that it could be as accurate.
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Acknowledgements
Research was sponsored by the Army Research Office and was accomplished under Grant Number W911NF-21-1-0264. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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Appendices
Appendix
A Full List of Features
The following table includes all the features that we collected, from which 13 were selected (Table 4).
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Nabavirazavi, S., Iyengar, S.S., Chaudhary, N.K. (2024). Lightweight Malicious Packet Classifier for IoT Networks. In: Patel, S.J., Chaudhary, N.K., Gohil, B.N., Iyengar, S.S. (eds) Information Security, Privacy and Digital Forensics. ICISPD 2022. Lecture Notes in Electrical Engineering, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-99-5091-1_11
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DOI: https://doi.org/10.1007/978-981-99-5091-1_11
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