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Authors: Yulexis Pacheco and Weiqing Sun

Affiliation: College of Engineering, University of Toledo, Toledo, Ohio, U.S.A.

Keyword(s): Adversarial Machine Learning, Deep Learning, Deep Neural Networks, Intrusion Detection Datasets.

Abstract: Studies have shown the vulnerability of machine learning algorithms against adversarial samples in image classification problems in deep neural networks. However, there is a need for performing comprehensive studies of adversarial machine learning in the intrusion detection domain, where current research has been mainly conducted on the widely available KDD’99 and NSL-KDD datasets. In this study, we evaluate the vulnerability of contemporary datasets (in particular, UNSW-NB15 and Bot-IoT datasets) that represent the modern network environment against popular adversarial deep learning attack methods, and assess various machine learning classifiers’ robustness against the generated adversarial samples. Our study shows the feasibility of the attacks for both datasets where adversarial samples successfully decreased the overall detection performance.

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Paper citation in several formats:
Pacheco, Y. and Sun, W. (2021). Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets. In Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-491-6; ISSN 2184-4356, SciTePress, pages 160-171. DOI: 10.5220/0010253501600171

@conference{icissp21,
author={Yulexis Pacheco. and Weiqing Sun.},
title={Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets},
booktitle={Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP},
year={2021},
pages={160-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010253501600171},
isbn={978-989-758-491-6},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP
TI - Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets
SN - 978-989-758-491-6
IS - 2184-4356
AU - Pacheco, Y.
AU - Sun, W.
PY - 2021
SP - 160
EP - 171
DO - 10.5220/0010253501600171
PB - SciTePress

- Science and Technology Publications, Lda.
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