Citation

Hanafiah, Muhammad Iffat and Pang, Ying Han and Zarakovitis, Charilaos C. and Lim, Heng Siong and Skordoulis, Dionysis and Nikolakakou, Christina D. and Ooi, Shih Yin and Hiew, Fu San (2026) TSM-NIDS: A time-series mixer-based intrusion detection system for IoT networks. MethodsX, 16. p. 103885. ISSN 2215-0161

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Abstract

The rapid proliferation of Internet of Things (IoT) devices in healthcare, manufacturing, and smart cities has introduced significant cybersecurity challenges. These devices present an attractive attack surface for cyber threats, making robust intrusion detection essential. Traditional Intrusion Detection Systems (IDS) analyse IoT network traffic data as independent instances, failing to capture important temporal dependencies, leading to suboptimal detection performance. To address this limitation, we propose TSM-NIDS, an adaptation of the TSMixer architecture for anomaly detection in IoT networks. While TSMixer has demonstrated exceptional performance in domains such as retail forecasting and energy demand prediction, its application to cybersecurity remains largely unexplored. TSM-NIDS employs an All-MLP (Multi-Layer Perceptron) design that performs both time mixing and feature mixing, enabling it to learn sequential patterns and cross-feature dependencies crucial for differentiating between regular and malicious traffic. We evaluate TSM-NIDS using the publicly available TON-IoT dataset, where it surpasses existing state-of-the-art approaches, showing its potential for enhancing IoT network security.

Item Type:	Article
Uncontrolled Keywords:	Internet of Things, Intrusion detection system, Artificial intelligence
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 May 2026 01:11
Last Modified:	04 May 2026 01:11
URII:	http://shdl.mmu.edu.my/id/eprint/15804

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