Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

TL;DR

This paper explores how feature engineering combined with quantum machine learning can improve malware detection, aiming to enhance generalization and reduce training time for classifiers.

Contribution

It introduces a hybrid quantum ML framework with feature selection strategies to improve malware classification efficiency and accuracy.

Findings

VQC with XGBoost features achieved 78.91% accuracy on simulator.

Average accuracy of 74% with IBM 5-qubit quantum hardware.

Feature selection reduces data size and training time.

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.