Threats to Adversarial Training for IDSs and Mitigation

Hassan Chaitou, Thomas Robert, Jean Leneutre, Laurent Pautet

2022

Abstract

Intrusion Detection Systems (IDS) are essential tools to protect network security from malicious traffic. IDS have recently made significant advancements in their detection capabilities through deep learning algorithms compared to conventional approaches. However, these algorithms are susceptible to new types of adversarial evasion attacks. Deep learning-based IDS, in particular, are vulnerable to adversarial attacks based on Generative Adversarial Networks (GAN). First, this paper identifies the main threats to the robustness of IDS against adversarial sample attacks that aim at evading IDS detection by focusing on potential weaknesses in the structure and content of the dataset rather than on its representativeness. In addition, we propose an approach to improve the performance of adversarial training by driving it to focus on the best evasion candidates samples in the dataset. We find that GAN adversarial attack evasion capabilities are significantly reduced when our method is used to strengthen the IDS.

Paper Citation

in Harvard Style

Chaitou H., Robert T., Leneutre J. and Pautet L. (2022). Threats to Adversarial Training for IDSs and Mitigation. In Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-590-6, pages 226-236. DOI: 10.5220/0011277600003283

in Bibtex Style

@conference{secrypt22,
author={Hassan Chaitou and Thomas Robert and Jean Leneutre and Laurent Pautet},
title={Threats to Adversarial Training for IDSs and Mitigation},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2022},
pages={226-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011277600003283},
isbn={978-989-758-590-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Threats to Adversarial Training for IDSs and Mitigation
SN - 978-989-758-590-6
AU - Chaitou H.
AU - Robert T.
AU - Leneutre J.
AU - Pautet L.
PY - 2022
SP - 226
EP - 236
DO - 10.5220/0011277600003283