PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier
Abstract
Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive infor mation. Traditional machine learning techniques struggle to per form well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and vari ational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows.
Cite
@article{arxiv.2503.01799,
title = {PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier},
author = {Md. Farhan Shahriyar and Gazi Tanbhir and Abdullah Md Raihan Chy and Mohammed Abdul Al Arafat Tanzin and Md. Jisan Mashrafi},
journal= {arXiv preprint arXiv:2503.01799},
year = {2025}
}
Comments
This paper has been accepted and presented at the 3rd International Conference on Intelligent Systems Advanced Computing and Communication (ISACC 2025)