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Feature vulnerability and robustness assessment against adversarial machine learning attacks

Mccarthy, Andrew; Andriotis, Panagiotis; Ghadafi, Essam; Legg, Phil

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Authors

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Dr Panos Andriotis Panagiotis.Andriotis@uwe.ac.uk
Senior Lecturer in Computer Forensics and Security

Essam Ghadafi Essam.Ghadafi@uwe.ac.uk
Senior Lecturer in Computer Science

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Dr Phil Legg Phil.Legg@uwe.ac.uk
Associate Professor in Cyber Security



Abstract

Whilst machine learning has been widely adopted for various domains, it is important to consider how such techniques may be susceptible to malicious users through adversarial attacks. Given a trained classifier, a malicious attack may attempt to craft a data observation whereby the data features purposefully trigger the classifier to yield incorrect responses. This has been observed in various image classification tasks, including falsifying road sign detection and facial recognition, which could have severe consequences in real-world deployment. In this work, we investigate how these attacks could impact on network traffic analysis, and how a system could perform misclassification of common network attacks such as DDoS attacks. Using the CICIDS2017 data, we examine how vulnerable the data features used for intrusion detection are to perturbation attacks using FGSM adversarial examples. As a result, our method provides a defensive approach for assessing feature robustness that seeks to balance between classification accuracy whilst minimising the attack surface of the feature space.

Citation

Mccarthy, A., Andriotis, P., Ghadafi, E., & Legg, P. (2021). Feature vulnerability and robustness assessment against adversarial machine learning attacks. In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)https://doi.org/10.1109/CyberSA52016.2021.9478199

Conference Name 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2021
Start Date Jun 14, 2021
End Date Jun 18, 2021
Acceptance Date Apr 16, 2021
Online Publication Date Jul 12, 2021
Publication Date Jul 12, 2021
Deposit Date Jun 1, 2021
Publicly Available Date Aug 13, 2021
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Book Title 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)
ISBN 9781665430920
DOI https://doi.org/10.1109/CyberSA52016.2021.9478199
Keywords Index Terms-adversarial learning; machine learning; network traffic analysis
Public URL https://uwe-repository.worktribe.com/output/7434849

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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works





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