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Conference proceeding
Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks Using a Malware Dataset: A Comparative Analysis
2023 IEEE International Conference on Software Services Engineering (SSE), pp.222-231
IEEE International Conference on Software Services Engineering (SSE) (Chicago, Illinois, USA, 07/02/2023–07/08/2023)
07/2023
Web of Science ID: WOS:001065963600026
Abstract
The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security-sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models' performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNN's accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.
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Details
- Title
- Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks Using a Malware Dataset
- Publication Details
- 2023 IEEE International Conference on Software Services Engineering (SSE), pp.222-231
- Resource Type
- Conference proceeding
- Conference
- IEEE International Conference on Software Services Engineering (SSE) (Chicago, Illinois, USA, 07/02/2023–07/08/2023)
- Publisher
- IEEE
- Number of pages
- 10
- Grant note
- 2209638,2100115 / National Science Foundation under NSF (10.13039/100000001)
- Copyright
- © 2023, IEEE
- Identifiers
- WOS:001065963600026; 99381460889406600
- Academic Unit
- Civil Engineering and Construction Management; Center for Cybersecurity; Hal Marcus College of Science and Engineering
- Language
- English