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2018 | OriginalPaper | Chapter

SU-IDS: A Semi-supervised and Unsupervised Framework for Network Intrusion Detection

Authors : Erxue Min, Jun Long, Qiang Liu, Jianjing Cui, Zhiping Cai, Junbo Ma

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

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Abstract

Network Intrusion Detection Systems (NIDSs) are increasingly crucial due to the expansion of computer networks. Detection techniques based on machine learning have attracted extensive attention for their capability to detect novel attacks. However, they require a large amount of labeled training data to train an effective model, which is difficult and expensive to obtain. To this effect, it is critically important to build models which can learn from unlabeled or partially-labeled data. In this paper, we propose an autoencoder-based framework, i.e., SU-IDS, for semi-supervised and unsupervised network intrusion detection. The framework augments the usual clustering (or classification) loss with an auxiliary loss of autoencoder, and thus achieves a better performance. The experimental results on the classic NSL-KDD dataset and the modern CICIDS2017 dataset show the superiority of our proposed models.

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Footnotes
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Metadata
Title
SU-IDS: A Semi-supervised and Unsupervised Framework for Network Intrusion Detection
Authors
Erxue Min
Jun Long
Qiang Liu
Jianjing Cui
Zhiping Cai
Junbo Ma
Copyright Year
2018
Book
Cloud Computing and Security

Print ISBN: 978-3-030-00011-0

Electronic ISBN: 978-3-030-00012-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00012-7_30

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