Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection Article Swipe
Mst Shapna Akter
,
Hossain Shahriar
,
Sheikh Iqbal Ahamed
,
Kishor Datta Gupta
,
Muhammad Sabbir Rahman
,
Atef Mohamed
,
Mohammad Ashiqur Rahman
,
Akond Rahman
,
Fan Wu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.00284
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.00284
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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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.00284
- https://arxiv.org/pdf/2306.00284
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379255920Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.00284Digital Object Identifier
- Title
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Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and ProtectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-06-01Full publication date if available
- Authors
-
Mst Shapna Akter, Hossain Shahriar, Sheikh Iqbal Ahamed, Kishor Datta Gupta, Muhammad Sabbir Rahman, Atef Mohamed, Mohammad Ashiqur Rahman, Akond Rahman, Fan WuList of authors in order
- Landing page
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https://arxiv.org/abs/2306.00284Publisher landing page
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https://arxiv.org/pdf/2306.00284Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2306.00284Direct OA link when available
- Concepts
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Malware, Computer science, Computer security, Novelty, Artificial intelligence, Machine learning, Philosophy, TheologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.experiences | 109 |
| abstract_inverted_index.protection. | 187 |
| abstract_inverted_index.technology. | 62 |
| abstract_inverted_index.architecture | 41 |
| abstract_inverted_index.introduction | 136 |
| abstract_inverted_index.cybersecurity | 33, 53, 76, 94, 143, 199 |
| abstract_inverted_index.classification | 157, 185 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |