Case Study-Based Approach of Quantum Machine Learning in Cybersecurity - Quantum Support Vector Machine for Malware Classification and Protection

Aug 5, 2026

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Quantum machine learning (QML) applies quantum computing to strengthen classical machine learning for complex, real-world problems, including cybersecurity challenges. Because QML remains novel and architecturally complex, accessible learning resources are limited for cybersecurity learners. This work develops QML-based learning modules using a student-centered case-study approach, supported by pre-lab, lab, and post-lab activities and open-source PennyLane on the Drebin-215 dataset. A quantum support vector machine (QSVM) is evaluated for malware classification and protection, reaching 95% accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Case study based learning model

QML and cybersecurity learning materials

Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection Mst Shapna Akter Dept. of Computer Science Kennesaw State University Kennesaw, USA [Email: makter2@students.kennesaw.edu](Email: makter2@students.kennesaw.edu) Kishor Datta Gupta Hossain Shahriar Dept. of Information Technology Kennesaw State University Kennesaw, USA Email: [hshahria@kennesaw.edu](hshahria@kennesaw.edu) Muhammad Rahman Dept. of Computer Science Clark Atlanta University Georgia, USA Email: [kgupta@cau.edu](kgupta@cau.edu) Dept. of Information Technology Clayton State University Morrow, USA [Email: muhammadrahman@clayton.edu](Email: muhammadrahman@clayton.edu) Sheikh Iqbal Ahamed Dept. of Computer Science Marquette University Wisconsin, USA Email: [sheikh.ahamed@marquette.edu](sheikh.ahamed@marquette.edu) Atef Mohamed Dept. of Information Technology Georgia Southern University Georgia, Country Email: [amohamed@georgiasouthern.edu](amohamed@georgiasouthern.edu) Mohammad Rahman Akond Rahman Dept. of Electrical and Computer Engineering Florida International University Florida, USA [Email: marahman@fiu.edu](Email: marahman@fiu.edu) Fan Wu Dept of Computer Science Tuskegee University Tuskegee, USA Email: [fwu@tuskegee.edu](fwu@tuskegee.edu) Abstract—Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex realworld problems. QML has the potential to address cybersecurityrelated challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. Index Terms—Quantum Machine Learning (QML), Quantum Support Vector Machine (QSVM), Cybersecurity, Malware Classification Dept. of Computer Science and Software Engineering Auburn University Auburn, USA Email: [akond@auburn.edu](akond@auburn.edu) [I. I](I. INTRODUCTION)[NTRODUCTION](I. INTRODUCTION) Case study based learning is a well-adopted learning approach in education and refers to learner-centered strategies and features intense interaction between participants as they increase their knowledge and analyze the case as a group. Moreover, students are involved in discussion of certain scenarios that approximate or are typically real-world examples [1] . Towards providing students with the crucial skills they need through hands-on experiences in solving real-world security problems, case study based learning provides a unique hands-on approach comprising pre-lab, lab, and post-lab activities [2] . Among those three activities, pre-lab demonstrates elementary concepts on a topic, which will help students gather prior knowledge required for doing experimental work. The next step comes with the lab, which provides the necessary instruction of hands-on practice. This step engages and inspires st

FAQs

What learning approach does the paper propose for teaching QML in cybersecurity?

It uses a student-centered case-study based learning approach with pre-lab, lab, and post-lab activities to guide learners through real-world security problem solving.

Which QML method is used for malware classification and protection?

The study applies a quantum support vector machine (QSVM) to perform malware classification and protection.

Which dataset and framework are used to evaluate the QSVM model?

The implementation uses the open-source PennyLane QML framework and evaluates on the Drebin-215 dataset.

Aug 5, 2026

Upload by Himbo