Abstract: The rapid proliferation of Internet of Things (IoT) devices has significantly increased the cybersecurity attack surface, making IoT environments highly vulnerable to sophisticated botnet-based attacks such as Mirai and Gafgyt (BASHLITE). Although conventional intrusion detection systems (IDS) have demonstrated promising performance, they often depend on large-scale labeled datasets and primarily focus on binary attack detection, limiting their applicability in realistic IoT environments characterized by scarce attack data and evolving threats. To address these challenges, this study proposes a hybrid quantum-classical intrusion detection framework, termed KPCA-VQC-XGBoost, for sample-efficient multiclass IoT botnet detection. The proposed approach integrates Kernel Principal Component Analysis (KPCA) for nonlinear feature reduction, a 10-qubit Variational Quantum Circuit (VQC) for quantum-enhanced feature extraction, and XGBoost for robust multiclass classification. The framework is evaluated on the N-BaIoT dataset to distinguish Benign, Gafgyt, and Mirai traffic classes using only 2,400 stratified training samples. Experimental results demonstrate that the proposed framework achieves 95.70% classification accuracy, a macro F1-score of 0.960, and Area Under Curve (AUC) values greater than 0.994 for all classes, indicating strong generalization under constrained training conditions. Comparative analysis further reveals that the proposed framework maintains competitive performance while substantially reducing training data requirements relative to existing quantum intrusion detection approaches. The findings highlight the potential of hybrid quantum-classical learning architectures for developing lightweight, scalable, and data-efficient cybersecurity solutions for next-generation IoT systems.

Key words: quantum machine learning, internet of things security, intrusion detection system, variational quantum circuit, IoT botnet detection