Quantum Machine Learning for Malware Classification

TL;DR

This paper explores the potential of quantum machine learning algorithms to improve malware classification, comparing their performance to classical models on a dataset of malicious and benign files.

Contribution

It introduces and evaluates two quantum machine learning models for malware detection, highlighting their potential advantages over classical approaches.

Findings

Quantum models show promise in malware classification tasks.

Comparison indicates potential benefits of quantum algorithms over classical methods.

Exploratory analysis suggests future research directions in quantum cybersecurity.

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.