Quantum machine learning-based intrusion detection system for IoT cloud-enabled smart city environments

Abstract

The fast integration of Internet of Things (IoT) devices and cloud platforms has altered current smart cities, allowing for intelligent services and effective communication. However, the increased interconnectedness has broadened the attack surface, exposing systems to complex, evolving cyberattacks that standard Intrusion Detection Systems (IDSs) cannot adequately counter. Classical Machine Learning architectures have constraints in handling high-dimensional data, achieving scalability, maintaining performance, and adapting to dynamic attack behaviours. To find these problems, this study presents a Quantum Machine Learning (QML)-based Intrusion Detection framework that uses Quantum Support Vector Machines (QSVM) to improve detection accuracy, adaptability, and computational efficiency in conceptual IoT Cloud-Enabled Smart City environments. The proposed model was assessed through simulation-based evaluation on the CICDDoS2019 dataset using the DrDoS DNS subset and compared against conventional Machine Learning Models such as SVM, XGBoost, Random Forest, and RNN. The experimental results show an overall performance improvement of 99.28%, with our research delivering significantly higher detection accuracy and system adaptability, along with fewer false alarms and faster response times. These results demonstrate the efficiency of Quantum-enhanced Learning in protecting IoT-Cloud infrastructures in smart-city environments. The proposed QSVM framework was evaluated using a classical quantum simulation environment. Therefore, the reported findings represent simulation-based observations and should not be interpreted as evidence of practical quantum computational advantage or hardware-based validation.