Paper Title: Hybrid Quantum Machine Learning for Threat Detection in Industrial Internet of Things
Conference Name: Korean Institute of Communications and Information Sciences (KICS Summer 2025) Conference
Abstract: Industrial internet of things (IIoT) networks are
often targeted by cyberattacks due to their economic importance
to a country. A quantum-classical hybrid deep learning model
P ure − HQM L is developed in this study to detect malicious
traffic in the IIoT network. To reduce complexity and enhance
model performance, this work employs analysis of variance F-test
(ANOVA F-test) statistical technique to select relevant features.
The results of our proposed model demonstrate exceptional
performance, achieving high accuracy and low-complexity model
structure in only a few epochs.