loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Hassan Chaitou ; Thomas Robert ; Jean Leneutre and Laurent Pautet

Affiliation: LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France

Keyword(s): Adversarial Machine Learning, GAN, Intrusion Detection System, Sensitivity Analysis.

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 use d to strengthen the IDS. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.139.236.93

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - SECRYPT; ISBN 978-989-758-590-6; ISSN 2184-7711, SciTePress, pages 226-236. DOI: 10.5220/0011277600003283

@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 - SECRYPT},
year={2022},
pages={226-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011277600003283},
isbn={978-989-758-590-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT
TI - Threats to Adversarial Training for IDSs and Mitigation
SN - 978-989-758-590-6
IS - 2184-7711
AU - Chaitou, H.
AU - Robert, T.
AU - Leneutre, J.
AU - Pautet, L.
PY - 2022
SP - 226
EP - 236
DO - 10.5220/0011277600003283
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic