Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

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2305.02396.pdf (425.68 KB)

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https://arxiv.org/abs/2305.02396

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https://doi.org/10.48550/arXiv.2305.02396
 http://hdl.handle.net/11603/28080

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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https://orcid.org/0000-0002-4362-0256
 https://orcid.org/0000-0001-9494-7139

Date

2023-05-03

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journal articles
preprints
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication

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Abstract

With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are supervised solutions. These supervised classifiers often do not generalize well to novel malware. Therefore, they need to be re-trained frequently to detect new malware specimens, which can be time-consuming. Our work addresses this problem in a hybrid framework of theoretical Quantum ML, combined with feature selection strategies to reduce the data size and malware classifier training time. The preliminary results show that VQC with XGBoost selected features can get a 78.91% test accuracy on the simulator. The average accuracy for the model trained using the features selected with XGBoost was 74% (+-11.35%) on the IBM 5 qubits machines.