Quantum-Enhanced IoT-Cloud Security: Integrating SHAP and Variational Quantum Classifiers

In the current Internet era, there are now trillions of gadgets online, and the Internet of Things is becoming a necessary part of daily life. IoT devices are connected, but this also makes them vulnerable to cyberattacks. Cyberattacks targeting Internet of Things (IoT) systems have increased dramatically in both volume and sophistication in the last year. Determining the importance and explainability of significant feature selection is not done in conventional feature selection, which acts as a black box method. Classical machine learning suffers from overload and class imbalance issues in IoT-based cloud security which is the major issue that results in botnet attack detection. To overcome these two issues, this study developed a model of a feature map to encode conventional data to a quantum feature space and then utilize the newly created quantum data in the cognitive circuit, which motivates to development of Quantum Shapley Additive Explanation with Variational Quantum Classifier (QSHAP-VQC) is implemented. This makes it possible to employ classical data in a quantum circuit. To minimize a cost function, a VQC employs hybrid quantum-classical techniques that involve parameterized circuits and gates whose parameters are improved via a classically based optimization loop. A quantum-classical hybrid loop consisting of these steps is eventually broken when the classical optimization finds the ideal parameters. For training data, the usual cost function is a comparison of the actual and expected outputs. The proposed QSHAP-VQC achieves the highest rate of accuracy in the detection of attacks in an IoT cloud environment.