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

• URL: http://arxiv.org/abs/2306.00284v1
• Date: Thu, 1 Jun 2023 02:04:09 GMT
• Title: Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection
• Authors: Mst Shapna Akter, Hossain Shahriar, Sheikh Iqbal Ahamed, Kishor Datta Gupta, Muhammad Rahman, Atef Mohamed, Mohammad Rahman, Akond Rahman, Fan Wu
• Abstract summary: We design and develop QML-based ten learning modules covering various cybersecurity topics. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection.
• Score: 8.34729912896717
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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