Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related 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.

翻译：量子机器学习（QML）是一个新兴研究领域，通过利用量子计算改进经典机器学习方法，以解决复杂的现实问题。QML在应对网络安全相关挑战方面具有潜力。考虑到QML的新颖性与复杂架构，目前尚未有明确可用的资源，能够帮助网络安全学习者有效掌握这一新兴技术的知识。在本研究中，我们采用以学生为中心的案例研究学习方法，设计并开发了十个涵盖不同网络安全主题的QML学习模块。我们将QML的一个子主题应用于网络安全问题，通过包含预实验、实验和实验后活动的环节，为学习者提供解决实际安全问题的动手QML体验。为了在鼓励所有学生参与的学习环境中激发和激励学生，预实验部分同时简要介绍了QML子主题与网络安全问题。本文利用量子支持向量机（QSVM）进行恶意软件分类与防护，在drebin215数据集上使用开源PennyLane QML框架进行实验。我们展示了QSVM模型，并在恶意软件分类与防护中实现了95%的准确率。我们将在未来开发所有模块，并将其介绍给网络安全社区。