Evidence map›Paper›PMID 41310009›Full record

ArticleScientific reports2025

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

Brij B Gupta, Shin-Hung Pan, Akshat Gaurav, Varsha Arya, Razaz Waheeb Attar, Amal Hassan Alhazmi, Ahmed Alhomoud, Kwok Tai Chui

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Brij B GuptaDepartment of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan. bbgupta@asia.edu.tw.
Shin-Hung PanDepartment of Information Management, Chaoyang University of Technology, Taichung, Taiwan. vincentpan@cyut.edu.tw.
Akshat GauravDepartment of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan.
Varsha AryaHong Kong Metropolitan University, Hong Kong SAR, China.
Razaz Waheeb AttarManagement Department, College of Business Administration, Princess Nourah bint Abdulrahman University, P.B. Box 84428, Riyadh, 11671, Saudi Arabia.
Amal Hassan AlhazmiManagement Department, College of Business Administration, Princess Nourah bint Abdulrahman University, P.B. Box 84428, Riyadh, 11671, Saudi Arabia.
Ahmed AlhomoudDepartment of Computer Science, College of Science, Northern Border University, Arar, 91431, Saudi Arabia.
Kwok Tai ChuiHong Kong Metropolitan University, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phishing websites continue to pose a significant cybersecurity threat, exploiting URL structures to deceive users and bypass traditional detection systems. Most existing models rely solely on classical deep learning, which often incurs high computational overhead and lacks interpretability. In this context, this paper proposes a quantum-enhanced hybrid AI model that integrates a classical neural encoder with a parameterized quantum circuit, leveraging quantum entanglement and superposition for enriched feature representation. The model processes URL-based features and achieves a classification accuracy of 97.3% with significantly reduced trainable parameters compared to baseline methods. The results confirm the model's effectiveness, efficiency, and potential in next-generation phishing detection systems.

Identifiers

PMID41310009
PMCPMC12660797

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.