Quantum Computing Methods for Malware Detection

Eliška Krátká () and Aurél Gábor Gábris ()
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Eliška Krátká: Czech Technical University in Prague, Faculty of Information Technology
Aurél Gábor Gábris: Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering

A chapter in Machine Learning, Deep Learning and AI for Cybersecurity, 2025, pp 207-228 from Springer

Abstract: Abstract In this paper, we explore the potential of quantum computing in enhancing malware detection through the application of Quantum Machine Learning (QML). Our main objective is to investigate the performance of the Quantum Support Vector Machine (QSVM) algorithm compared to SVM. A publicly available dataset containing raw binaries of Portable Executable (PE) files was used for the classification. The QSVM algorithm, incorporating quantum kernels through different feature maps, was implemented and evaluated on a local simulator within the Qiskit SDK and IBM quantum computers. Experimental results from simulators and quantum hardware provide insights into the behavior and performance of quantum computers, especially in handling large-scale computations for malware detection tasks. The work summarizes the practical experience with using quantum hardware via the Qiskit interfaces. We describe in detail the critical issues encountered, as well as the fixes that had to be developed and applied to the base code of the Qiskit Machine Learning library. These issues include missing transpilation of the circuits submitted to IBM Quantum systems and exceeding the maximum job size limit due to the submission of all the circuits in one job.

Date: 2025
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DOI: 10.1007/978-3-031-83157-7_8

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