Quantum Machine Learning for Detecting Known Cyber-Attacks in IoT Networks
Polasi Sudhakar, D Ratnagiri, Chokkapu Bhargavi, Sape Chittibabulu, G. Satyanarayana, Amanulla Mohammad · 2025
The rapid growth of Internet of Things (IoT) devices has exposed weaknesses in network infrastructures, so efficient and effective cyberattack detection is absolutely important. The application of Quantum Machine Learning (QML), more especially Quantum Support Vector Machines (QSVM), to raise the detection accuracy of known cyberthreats in IoT environments is investigated in this work. To better manage the enormous dimensionality and complexity of IoT network traffic data than conventional approaches, QSVM uses quantum computing ideas including improved quantum feature encoding and kernel techniques. With an accuracy of 99.12% against 92.00%, along with improved precision, recall, and F1-score metrics, experimental data show that QSVM greatly beats the classical Support Vector Machine (SVM). An essential first step toward integrating quantum computing into practical cybersecurity applications, our results show the potential of quantum-enhanced classifiers to deliver strong, scalable, and highly accurate intrusion detection systems for IoT networks.