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The burgeoning fields of machine learning (ML) and quantum machine learning(QML) have shown remarkable potential in tackling complex problems acrossvarious domains. However, their susceptibility to adversarial attacks raisesconcerns when deploying these systems in security sensitive applications. Inthis study, we present a comparative analysis of the vulnerability of ML andQML models, specifically conventional neural networks (NN) and quantum neuralnetworks (QNN), to adversarial attacks using a malware dataset. We utilize asoftware supply chain attack dataset known as ClaMP and develop two distinctmodels for QNN and NN, employing Pennylane for quantum implementations andTensorFlow and Keras for traditional implementations. Our methodology involvescrafting adversarial samples by introducing random noise to a small portion ofthe dataset and evaluating the impact on the models performance using accuracy,precision, recall, and F1 score metrics. Based on our observations, both ML andQML models exhibit vulnerability to adversarial attacks. While the QNNsaccuracy decreases more significantly compared to the NN after the attack, itdemonstrates better performance in terms of precision and recall, indicatinghigher resilience in detecting true positives under adversarial conditions. Wealso find that adversarial samples crafted for one model type can impair theperformance of the other, highlighting the need for robust defense mechanisms.Our study serves as a foundation for future research focused on enhancing thesecurity and resilience of ML and QML models, particularly QNN, given itsrecent advancements. A more extensive range of experiments will be conducted tobetter understand the performance and robustness of both models in the face ofadversarial attacks.

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