Detection of Physiological Data Tampering Attacks with Quantum Machine Learning
This addresses the need for reliable healthcare systems by evaluating QML for data tampering detection, though it is incremental as it compares existing methods on new data.
The study tackled the problem of detecting physiological data tampering attacks by comparing Quantum Machine Learning (QML) to classical methods, finding that QML achieved 75%-95% accuracy for label-flipping attacks but only 45%-65% for adversarial perturbations.
The widespread use of cloud-based medical devices and wearable sensors has made physiological data susceptible to tampering. These attacks can compromise the reliability of healthcare systems which can be critical and life-threatening. Detection of such data tampering is of immediate need. Machine learning has been used to detect anomalies in datasets but the performance of Quantum Machine Learning (QML) is still yet to be evaluated for physiological sensor data. Thus, our study compares the effectiveness of QML for detecting physiological data tampering, focusing on two types of white-box attacks: data poisoning and adversarial perturbation. The results show that QML models are better at identifying label-flipping attacks, achieving accuracy rates of 75%-95% depending on the data and attack severity. This superior performance is due to the ability of quantum algorithms to handle complex and high-dimensional data. However, both QML and classical models struggle to detect more sophisticated adversarial perturbation attacks, which subtly alter data without changing its statistical properties. Although QML performed poorly against this attack with around 45%-65% accuracy, it still outperformed classical algorithms in some cases.