AAG: Adversarial Attack Generator for evaluating the robustness of Machine Learning Models against Adversarial Attacks

With the ongoing integration of machine learning models into critical infrastructure, the resilience of these systems against adversarial attacks is important for all domains. This paper introduces an adversarial attack generator framework against a network dataset that is part of OCPP Dataset using CI-CFlowMeter parser. We conduct a comprehensive evaluation of various prominent adversarial attacks, including FGSMA, JSMA, PGD, C&W, and more to assess their efficacy on the OCCP dataset. The Adversarial Generator is meticulously evaluated, demonstrating a significant impact in the models performance to detect potential perturbations. The results showcased the impact of the different type of adversarial attacks, contributing to a critical advancement in future defense strategies that need to be utilised in order to protect industrial control systems.

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Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10826110
Citations
1
Primary Topic
Adversarial Robustness in Machine Learning
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article
Field-Weighted Citation Impact
0.35
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AAG: Adversarial Attack Generator for evaluating the robustness of Machine Learning Models against Adversarial Attacks

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AAG: Adversarial Attack Generator for evaluating the robustness of Machine Learning Models against Adversarial Attacks

Evangelos Markakis, Vasileios Argyriou, Panagiotis Radoglou‐Grammatikis, Ioannis D. Moscholios, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos, Θωμάς Λάγκας, Dimitrios Christos Asimopoulos, Jorgen Cani
article en
1 citations

Abstract

With the ongoing integration of machine learning models into critical infrastructure, the resilience of these systems against adversarial attacks is important for all domains. This paper introduces an adversarial attack generator framework against a network dataset that is part of OCPP Dataset using CI-CFlowMeter parser. We conduct a comprehensive evaluation of various prominent adversarial attacks, including FGSMA, JSMA, PGD, C&W, and more to assess their efficacy on the OCCP dataset. The Adversarial Generator is meticulously evaluated, demonstrating a significant impact in the models performance to detect potential perturbations. The results showcased the impact of the different type of adversarial attacks, contributing to a critical advancement in future defense strategies that need to be utilised in order to protect industrial control systems.

Democritus University of Thrace (GR), University of Peloponnese (GR), International Hellenic University (GR), Kingston University London (GB), Hellenic Mediterranean University (GR), Harokopio University of Athens (GR), University of Western Macedonia (GR)
Openalex Percentile: Top 30%
Adversarial Robustness in Machine Learning
0.35
DOIOpen Access PDFOpenAlex
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