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

arXiv: https://arxiv.org/abs/2306.08060 · HTML: https://mlsanity.com/paper/2306.08060/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 8 of 8)

- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Quantum Neural Networks: Concepts, Applications, and Challenges](https://mlsanity.com/paper/2108.01468/) (2021)
- [Malware Detection and Prevention using Artificial Intelligence Techniques](https://mlsanity.com/paper/2206.12770/) (2022)
- [Ransomware Classification and Detection With Machine Learning Algorithms](https://mlsanity.com/paper/2207.00894/) (2022)
- [Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection](https://mlsanity.com/paper/2207.09902/) (2022)
- [A Review of Quantum Cybersecurity: Threats, Risks and Opportunities](https://mlsanity.com/paper/2207.03534/) (2022)
- [Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?](https://mlsanity.com/paper/2204.02784/) (2022)

## Built on by (most cited first, 4 of 4)

- [Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks](https://mlsanity.com/paper/2306.00283/) (2023)
- [Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection](https://mlsanity.com/paper/2306.00284/) (2023)
- [Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks](https://mlsanity.com/paper/2306.07981/) (2023)
- [A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data](https://mlsanity.com/paper/2308.09722/) (2023)

## Abstract (arXiv)

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.