SSL-FSL Framework for Detecting Unseen IoT Attacks under Limited Labeled Data

Abualhassan, Amer A., Al-Awami, Louai and Hamdan Mohamed, Mosab (2026) SSL-FSL Framework for Detecting Unseen IoT Attacks under Limited Labeled Data. In: 2026 IFIP Networking Conference, IFIP Networking 2026. IEEE, Lugano, Switzerland. ISBN 978-390317682-9

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Abstract

Traditional intrusion detection system solutions and modern machine learning (ML) and deep learning (DL)-based approaches depend on large labeled datasets, which makes them less effective in detecting rare attacks in Internet of Things (IoT) networks. This paper addresses these limitations by proposing a self-supervised few-shot learning (SSL-FSL) intrusion detection framework that enables data-efficient learning and adaptive recognition of unseen attack types using only a small number of labeled samples. The model will be trained and tested on the Edge-IIoTset dataset as well as on unseen attack categories from the CICIoT2023 dataset. The traffic data will be represented as images to allow the model to learn spatial feature patterns rather than relying on class-specific characteristics. This representation, combined with self-supervised pre-training and prototype-based few-shot adaptation, enables the extraction of robust and transferable embeddings that capture intrinsic relationships within the data. Experimental results demonstrate that the proposed SSL-FSL framework achieves strong unseen attack detection performance and achieves an accuracy of 69.76% on the CICIoT2023 dataset without any fine-tuning, highlighting its ability to generalize to novel attack types. Overall, the proposed framework provides a practical, data-efficient, and generalizable solution for adaptive intrusion detection in heterogeneous IoT environments.

Item Type:	Book Section
Uncontrolled Keywords:	deep learning; few-shot learning; Internet of Things; intrusion detection system; limited labeled data; network security; self-supervised learning
Subjects:	Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	24 Jul 2026 13:26
Last Modified:	24 Jul 2026 13:26
URI:	https://norma.ncirl.ie/id/eprint/9482

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