Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis Article Swipe

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Mst Shapna Akter , Hossain Shahriar , Iysa Iqbal , M. Belal Hossain , M. A. Karim , Victor Clincy , Razvan Voicu · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2305.19593

The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNNs accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.

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preprint

Language

en

Landing Page

http://arxiv.org/abs/2305.19593

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https://arxiv.org/pdf/2305.19593

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green

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10

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https://openalex.org/W4379052715

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https://openalex.org/W4379052715

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DOI

https://doi.org/10.48550/arxiv.2305.19593

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Title

Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis

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Type

preprint

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Language

en

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Publication year

2023

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Publication date

2023-05-31

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Mst Shapna Akter, Hossain Shahriar, Iysa Iqbal, M. Belal Hossain, M. A. Karim, Victor Clincy, Razvan Voicu

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https://arxiv.org/abs/2305.19593

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https://arxiv.org/pdf/2305.19593

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Yes

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green

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https://arxiv.org/pdf/2305.19593

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Concepts

Computer science, Adversarial system, Artificial intelligence, Implementation, Machine learning, Robustness (evolution), Malware, Deep learning, Vulnerability (computing), Resilience (materials science), Artificial neural network, False positive paradox, Software, Computer security, Software engineering, Programming language, Chemistry, Physics, Gene, Biochemistry, Thermodynamics

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0

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10

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