Case Study-Based Approach of Quantum Machine Learning in Cybersecurity:
  Quantum Support Vector Machine for Malware Classification and Protection
Mst Shapna Akter Hossain Shahriar Sheikh Iqbal Ahamed Kishor Datta Gupta Muhammad Rahman Atef Mohamed Mohammad Rahman Akond Rahman Fan Wu
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.