Internet of Things Network Intrusion Detection System Using Quantum and Classical Machine Learning

ABSTRACT Quantum technology strengthens intrusion detection with unbreakable encryption and highly precise threat sensors, providing unparallelled security against cyber threats. This work proposes the integration of quantum technology into Network Intrusion Detection Systems (IDS), focusing on Machine Learning (ML) algorithms such as Quantum Support Vector Classifier (QSVC) and Quantum Random Forest (QRF) models in comparison with Classical Support Vector Classifier (CSVC) and Classical Random Forest (CRF). The dataset used in all the models is UNSW‐NB15. By harnessing vast amounts of UNSW‐NB15 data, it is processed in parallel by allowing quantum technology and thereby improving computer power and speed. The results are observed simultaneously by simulating QSVC and QRF in IBM Quantum labs, which shows improved performance of and compared to classical CSVC and CRF methods, resulting in accuracy. Later, the QSVC and QRF are processed with an optimiser to obtain improved accuracy of for QSVC and for QRF. The proposed framework aims to enhance the resistance and efficiency of IDS in defending against evolutionary cyber threats in a precisely interconnected digital ecosystem.