JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

A subspace-based few-shot intrusion detection system for the Internet of Things

Author(s): Zhihui LI^1, 2, Congyuan XU², Kun DENG², Chunyuan LIU²
Affiliation(s): ¹School of Information Science and Engineering,Zhejiang Sci-Tech University, Hangzhou 310027, China; more
Corresponding email(s): cyxu@zjxu.edu.cn
Key Words: Intrusion detection system; Few-shot learning; Internet of Things; Subspace

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Zhihui LI^1,2, Congyuan XU², Kun DENG², Chunyuan LIU². A subspace-based few-shot intrusion detection system for the Internet of Things[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400556

@article{title="A subspace-based few-shot intrusion detection system for the Internet of Things",
author="Zhihui LI^1,2, Congyuan XU², Kun DENG², Chunyuan LIU²",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400556"
}

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doi="https://doi.org/10.1631/FITEE.2400556"

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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Deep learning-based intrusion detection systems rely on numerous training samples to achieve satisfactory detection rates. However, in real-world Internet of Things (IoT) environments, the diversity of IoT devices and the subsequent fragmentation of attack types result in a limited number of training samples, which urgently requires researchers to develop few-shot intrusion detection systems. In this study, we propose a subspace-based approach for few-shot IoT intrusion detection systems to cope with the dilemma of insu?cient learnable samples. The method is based on the principle of classifying metrics to identify network tra?c. After feature extraction of samples, a subspace is constructed for each category. Next, the distance between the query samples and the subspace is calculated by the metric module, thus detecting malicious samples. Subsequently, based on the CICIOT2023 dataset we constructed a few-shot IoT intrusion-detection dataset and evaluated the proposed method. For the detection of unknown categories, the detection accuracies were 93.52% in the 5-way 1-shot setting, 92.99% in the 5-way 5-shot setting and 93.65% in the 5-way 10-shot setting.

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