# PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier

arXiv: https://arxiv.org/abs/2503.01799 · HTML: https://mlsanity.com/paper/2503.01799/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [QML-IDS: Quantum Machine Learning Intrusion Detection System](https://mlsanity.com/paper/2410.16308/) (2024)

## Built on by (most cited first, 1 of 1)

- [Quantum Machine Learning for Image Classification: A Hybrid Model of Residual Network with Quantum Support Vector Machine](https://mlsanity.com/paper/2510.23659/) (2025)

## Abstract (arXiv)

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.