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Phishing threat mitigation in E-commerce using a quantum-enhanced hybrid AI framework.

Brij B Gupta1,2,3,4,5, Shin-Hung Pan6, Akshat Gaurav7,8

  • 1Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan. bbgupta@asia.edu.tw.

Scientific Reports
|November 27, 2025
PubMed
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This study introduces a quantum-enhanced AI model for detecting phishing websites. The novel approach achieves high accuracy (97.3%) with fewer parameters, offering an efficient cybersecurity solution.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Quantum Computing

Background:

  • Phishing websites exploit URL structures, posing a significant cybersecurity threat.
  • Traditional deep learning models for phishing detection have high computational costs and lack interpretability.

Purpose of the Study:

  • To propose a novel quantum-enhanced hybrid AI model for improved phishing website detection.
  • To leverage quantum computing principles for enriched feature representation in cybersecurity.

Main Methods:

  • Integration of a classical neural encoder with a parameterized quantum circuit.
  • Utilizing quantum entanglement and superposition for enhanced feature extraction from URL data.
  • Developing a hybrid AI model for URL-based phishing detection.

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Main Results:

  • Achieved a classification accuracy of 97.3% in identifying phishing websites.
  • Demonstrated significantly reduced trainable parameters compared to baseline deep learning methods.
  • Confirmed the model's effectiveness and computational efficiency.

Conclusions:

  • The quantum-enhanced hybrid AI model shows significant potential for next-generation phishing detection systems.
  • The proposed model offers an effective and efficient alternative to traditional deep learning approaches.
  • Quantum AI integration can enhance cybersecurity defenses against sophisticated threats.