@inproceedings{c62b18bcef5e4e51adf6d88de487ce19,
title = "Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs",
abstract = "Graph neural network-based network intrusion detection systems have recently demonstrated state-of-the-art performance on benchmark datasets. Nevertheless, these methods suffer from a reliance on target encoding for data pre-processing, limiting widespread adoption due to the associated need for annotated labels - a cost-prohibitive requirement. In this work, we propose a solution involving in-context pre-training and the utilization of dense representations for categorical features to jointly overcome the label-dependency limitation. Our approach exhibits remarkable data efficiency, achieving over 98% of the performance of the supervised state-of-the-art with less than 4% labeled data on the NF-UQ-NIDS-V2 dataset.",
keywords = "few-shot learning, graph neural network, Intrusion detection, machine learning, NIDS, self-supervised learning",
author = "Zhengyao Gu and Lopez, {Diego Troy} and Lilas Alrahis and Ozgur Sinanoglu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 25th International Symposium on Quality Electronic Design, ISQED 2024 ; Conference date: 03-04-2024 Through 05-04-2024",
year = "2024",
doi = "10.1109/ISQED60706.2024.10528371",
language = "English (US)",
series = "Proceedings - International Symposium on Quality Electronic Design, ISQED",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of the 25th International Symposium on Quality Electronic Design, ISQED 2024",
}