Can Feature Engineering Help Quantum Machine Learning for Malware Detection?
This work addresses the need for faster and more generalizable malware detection systems, though it appears incremental as it builds on existing quantum and classical methods.
The paper tackled the problem of malware detection by combining quantum machine learning with feature selection to reduce training time and improve generalization, achieving 78.91% test accuracy on a simulator and 74% average accuracy on IBM quantum hardware.
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.