Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

Authors: Ran Liu, Maksim Eren, Charles Nicholas
Published: 2023-05-03 | Updated: 2023-08-09

Source: https://arxiv.org/abs/2305.02396

PDF: https://arxiv.org/pdf/2305.02396

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量子ビットの特性 量子分類器 一般化の影響

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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 trained using the features selected with XGBoost was 74 5 qubits machines.