Software Supply Chain Vulnerabilities Detection in Source Code:
  Performance Comparison between Traditional and Quantum Machine Learning
  Algorithms
Mst Shapna Akter Md Jobair Hossain Faruk Nafisa Anjum Mohammad Masum Hossain Shahriar Akond Rahman Fan Wu Alfredo Cuzzocrea
The software supply chain (SSC) attack has become one of the crucial issues
that are being increased rapidly with the advancement of the software
development domain. In general, SSC attacks execute during the software
development processes lead to vulnerabilities in software products targeting
downstream customers and even involved stakeholders. Machine Learning
approaches are proven in detecting and preventing software security
vulnerabilities. Besides, emerging quantum machine learning can be promising in
addressing SSC attacks. Considering the distinction between traditional and
quantum machine learning, performance could be varies based on the proportions
of the experimenting dataset. In this paper, we conduct a comparative analysis
between quantum neural networks (QNN) and conventional neural networks (NN)
with a software supply chain attack dataset known as ClaMP. Our goal is to
distinguish the performance between QNN and NN and to conduct the experiment,
we develop two different models for QNN and NN by utilizing Pennylane for
quantum and TensorFlow and Keras for traditional respectively. We evaluated the
performance of both models with different proportions of the ClaMP dataset to
identify the f1 score, recall, precision, and accuracy. We also measure the
execution time to check the efficiency of both models. The demonstration result
indicates that execution time for QNN is slower than NN with a higher
percentage of datasets. Due to recent advancements in QNN, a large level of
experiments shall be carried out to understand both models accurately in our
future research.