Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis
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 QNNs
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.