Scalable Malware Family Classification Using Quantum Kernel Based Machine Learning

2026-06-15

AI Summary

This study addresses the limited accuracy of traditional machine learning approaches in classifying large-scale, structurally homogeneous malware into multiple families. To overcome this challenge, the authors propose a scalable quantum kernel machine learning framework that integrates structural features extracted from executable files, dimensionality reduction via Linear Discriminant Analysis, and a fidelity-based quantum kernel. Notably, the method introduces the Nyström low-rank approximation for the first time in this context to circumvent the quadratic computational complexity inherent in classical kernel methods. Evaluated on a dataset comprising 18,836 samples across 23 malware families, the proposed model achieves a classification accuracy of 80.88%, significantly outperforming classical baselines while maintaining both high precision and computational efficiency.

Abstract

The classification of malware families is a key challenge in cybersecurity, which enables threat attribution, analysis of attack operations, and the formulation of effective defense strategies. Emerging malware samples are becoming increasingly structurally similar and obfuscated, making accurate multiclass classification challenging for traditional machine learning models, especially when deployed at scale. In this research, we propose a scalable Quantum Kernel-based Machine Learning (QKML) framework for malware family classification that addresses both accuracy and efficiency constraints. The proposed framework extracts structural features from executable files and uses a supervised Linear Discriminant Analysis (LDA) projection to generate a compact, class-aware representation well suited for quantum processing. The nonlinear relationships among malware families are captured using a fidelity-based quantum kernel built from parameterized quantum circuits. We use the Nyström approximation method to obtain a low-rank approximation of the quantum kernel, which enables effective multiclass classification via ridge regression and enables learning from all available training samples without incurring the quadratic computational cost of kernel matrix construction. The proposed model achieves strong classification performance, with 80.88% accuracy, outperforming classical machine learning baselines under identical feature and data splits, according to experimental evaluation on a large-scale malware dataset that includes 18,836 samples across 23 malware families. These findings suggest that scalable quantum-kernel-based machine learning can offer measurable performance advantages for real-world malware family classification tasks.

Problem

• malware family classification
• cybersecurity
• scalable classification
• obfuscated malware
• multiclass classification

Innovation

• Quantum Kernel
• Malware Family Classification
• Nyström Approximation
• Parameterized Quantum Circuits
• Scalable Machine Learning