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Towards realistic problem-space adversarial attacks against machine learning in network intrusion detection

Authors:

Antonio Pecchia,

Antonio Repola,

Umberto VillanoAuthors Info & Claims

ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security

Article No.: 113, Pages 1 - 8

https://doi.org/10.1145/3664476.3669974

Published: 30 July 2024 Publication History

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Abstract

Current trends in network intrusion detection systems (NIDS) capitalize on the extraction of features from network traffic and the use of up-to-date machine and deep learning techniques to infer a detection model; in consequence, NIDS can be vulnerable to adversarial attacks. Differently from the plethora of contributions that apply (and misuse) feature-level attacks envisioned in application domains far from NIDS, this paper proposes a novel approach to adversarial attacks, which consists in a realistic problem-space perturbation of the network traffic. The perturbation is achieved through a traffic control utility. Experiments are based on normal and Denial of Service traffic in both legitimate and adversarial conditions, and the application of four popular techniques to learn the NIDS models. The results highlight the transferability of the adversarial examples generated by the proposed problem-space attack as well as the effectiveness at inducing traffic misclassifications across the NIDS models obtained.

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Cited By

Valente EOsti AP. Júnior LEstrella J(2024)DoH Deception: Evading ML-Based Tunnel Detection Models with Real-world Adversarial ExamplesAnais do XXIV Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg 2024)10.5753/sbseg.2024.241637(287-302)Online publication date: 16-Sep-2024
https://doi.org/10.5753/sbseg.2024.241637

Index Terms

Towards realistic problem-space adversarial attacks against machine learning in network intrusion detection
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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cover image ACM Other conferences

ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security

July 2024

2032 pages

ISBN:9798400717185

DOI:10.1145/3664476

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 July 2024

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Ministero dell'Università e della Ricerca

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ARES 2024

ARES 2024: The 19th International Conference on Availability, Reliability and Security

July 30 - August 2, 2024

Vienna, Austria

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Overall Acceptance Rate 228 of 451 submissions, 51%

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Cited By

Valente EOsti AP. Júnior LEstrella J(2024)DoH Deception: Evading ML-Based Tunnel Detection Models with Real-world Adversarial ExamplesAnais do XXIV Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg 2024)10.5753/sbseg.2024.241637(287-302)Online publication date: 16-Sep-2024
https://doi.org/10.5753/sbseg.2024.241637

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