Abstract :

The Increasing Sophistication Of Phishing Attacks Creates Significant Challenges For Conventional Cybersecurity Detection Systems. Traditional Machine Learning Approaches May Struggle To Identify Evolving Phishing Patterns And Previously Unseen Malicious Websites. This Paper Proposes An Adaptive Hybrid Quantum-Classical Learning Framework For Intelligent Phishing Website Detection And Cyber Threat Classification. The Proposed Framework Combines Classical Machine Learning Algorithms, Including Random Forest, Support Vector Machine, And XGBoost, With Quantum Machine Learning Techniques Such As Variational Quantum Classifier (VQC) And Quantum Kernel Support Vector Machine (QSVM). URL-based And Website-related Features, Including URL Length, Domain Characteristics, HTTPS Usage, Suspicious Symbols, Subdomain Patterns, And Redirection Indicators, Are Extracted During The Preprocessing Stage. The Selected Features Undergo Normalization, Feature Selection, And Dimensionality Reduction To Improve Classification Efficiency. Quantum Feature Maps Transform Selected Classical Features Into Quantum Representations For Classification. The Framework Evaluates The Performance Of Classical And Quantum Models Using Accuracy, Precision, Recall, F1-score, False-positive Rate, Computational Time, And Quantum Noise Robustness. The Study Investigates The Effectiveness And Limitations Of Hybrid Quantum-classical Learning For Improving Phishing Detection Accuracy, Reducing Classification Errors, And Supporting Reliable And Adaptive Cybersecurity Solutions.

Published:

02-11-2026

Issue:

Vol. 26 No. 11 (2026)

Page Nos:

1 - 12

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite