Lightweight TinyML NIDS for IoT devices

Mohd Yusoff, Siti Sarah and Islam, Md. Rafiqul and Habaebi, Mohamed Hadi and Najeeb, Athaur Rahman (2026) Lightweight TinyML NIDS for IoT devices. In: 2025 10th International Conference on Computer and Communication Engineering (ICCCE), 26-27 August 2025, KOE, IIUM.

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Abstract

With the increase in the implementation of Internet of Things (IoT) devices, this has introduced some serious and critical security challenges, specifically with regards to the resource-constrained environments, which are IoT devices that are used by the public for convenience. Furthermore, traditional intrusion detection systems (IDS) are too computationally intensive for such devices, emphasizing on the need for a much lightweight and efficient alternatives. This paper focuses on developing a lightweight IDS using TinyML to tackle these security challenges and ensure safety in IoT devices. Public IoT datasets were preprocessed to enhance the quality of datasets, and it is followed by feature importance and class distribution analyses. The machine learning models, K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression, were trained for intrusion detection. The models were then converted to C++ code format for deployment on a Raspberry Pi simulation. Testing and evaluation were conducted using opensource Quick Emulator (QEMU), which uses dynamic binary translation to emulate a computer’s processor. QEMU provides a variety of hardware and device models for virtual machines, enabling it to run different guest operating systems, making it an ideal option. The testing and evaluation processes were conducted to assess performance using metrics like accuracy, precision, recall, and resource utilization. The results demonstrate that the proposed system achieves high detection accuracy with minimal computational overhead, making it a viable solution for securing resource-constrained IoT devices. This study underscores the potential of TinyML in improving and enhancing IoT security while ensuring scalability as well as efficiency.

Item Type:	Proceeding Paper (Plenary Papers)
Uncontrolled Keywords:	Intrusion Detection System (IDS), Tiny Machine Learning (TinyML), Internet of Things (IoT), Machine Learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	29 Apr 2026 16:40
Last Modified:	29 Apr 2026 16:40
Queue Number:	2026-04-Q2959
URI:	http://irep.iium.edu.my/id/eprint/128496

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