Quantum Computing Methods for Malware Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00385480" target="_blank" >RIV/68407700:21240/25:00385480 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21340/25:00385480

Result on the web

<a href="https://doi.org/10.1007/978-3-031-83157-7_8" target="_blank" >https://doi.org/10.1007/978-3-031-83157-7_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-83157-7_8" target="_blank" >10.1007/978-3-031-83157-7_8</a>

Result language

angličtina

Original language name

Quantum Computing Methods for Malware Detection

Original language description

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.

Czech name

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Czech description

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Type

C - Chapter in a specialist book

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name

Machine Learning, Deep Learning and AI for Cybersecurity

ISBN

978-3-031-83156-0

Number of pages of the result

22

Pages from-to

207-228

Number of pages of the book

642

Publisher name

Springer Nature Switzerland AG

Place of publication

Basel

UT code for WoS chapter

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