Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms-Reference-Cited by-同舟云学术

Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Author:

Akter Mst Shapna¹,Faruk Md Jobair Hossain¹,Anjum Nafisa²,Masum Mohammad³,Shahriar Hossain²,Sakib Nazmus²,Rahman Akond⁴,Wu Fan⁵,Cuzzocrea Alfredo⁶

Affiliation:

1. Kennesaw State University,Department of Computer Science,USA

2. Kennesaw State University,Department of Information Technology,USA

3. San Jose State University,Department of Applied Data Science,USA

4. Auburn University,Department of Computer Science and Software Engineering,USA

5. Tuskegee University,Department of Computer Science,USA

6. University of Calabria,iDEA Lab,Rende,Italy

Funder

National Science Foundation

Publisher

IEEE

Link

Reference34 articles.

4. Defending Against Software Supply Chain Attacks,2021

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1. Quantum Exploration in Ransomware Detection with Conventional Machine Learning Approaches;2024 IEEE International Conference on Contemporary Computing and Communications (InC4);2024-03-15

2. Securing the Software Supply Chain: A New Taxonomy for Attack Classification;2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE);2023-12-14