Quantum Machine Learning for Malware Classification

Authors: Grégoire Barrué, Tony Quertier
Published: 2023-05-09 | Updated: 2023-06-07

Source: https://arxiv.org/abs/2305.09674

PDF: https://arxiv.org/pdf/2305.09674

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量子ニューラルネットワーク パラメータ調整 機械学習手法

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Abstract

In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks. We implement two models of Quantum Machine Learning algorithms, and we compare them to classical models for the classification of a dataset composed of malicious and benign executable files. We try to optimize our algorithms based on methods found in the literature, and analyze our results in an exploratory way, to identify the most interesting directions to explore for the future.