Integrating AI and Semantic Web Technologies for Robust Phishing Detection in Virtual Realities Article Swipe
Liang Zhou
,
Akshat Gaurav
,
Wadee Alhalabi
,
Varsha Arya
,
Eaman Alharbi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.4018/ijswis.371415
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.4018/ijswis.371415
Phishing detection is a critical challenge in virtual realities, where malicious activities can compromise user security. This paper presents a novel approach integrating AI and Semantic Web Technologies for robust phishing detection. The proposed model preprocesses text data and leverages a reduced six-layer BERT encoder to extract contextual embeddings. Outputs from BERT, including classifier, attention, and encoder layers, are combined with features derived from Semantic Web Technologies and a custom deep learning layer to form a unified representation. The concatenated features are passed to a linear layer for classification. Experiments demonstrate superior performance, achieving 95\% accuracy, 96\% F1-score, and a 0.99 ROC-AUC, outperforming standard machine learning models. This framework provides a reliable phishing detection solution for virtual environments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4018/ijswis.371415
- https://www.igi-global.com/ViewTitle.aspx?TitleId=371415&isxn=9798337311630
- OA Status
- diamond
- Cited By
- 3
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408434153
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408434153Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4018/ijswis.371415Digital Object Identifier
- Title
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Integrating AI and Semantic Web Technologies for Robust Phishing Detection in Virtual RealitiesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-14Full publication date if available
- Authors
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Liang Zhou, Akshat Gaurav, Wadee Alhalabi, Varsha Arya, Eaman AlharbiList of authors in order
- Landing page
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https://doi.org/10.4018/ijswis.371415Publisher landing page
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https://www.igi-global.com/ViewTitle.aspx?TitleId=371415&isxn=9798337311630Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.igi-global.com/ViewTitle.aspx?TitleId=371415&isxn=9798337311630Direct OA link when available
- Concepts
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Computer science, Phishing, World Wide Web, Semantic Web, Social Semantic Web, Information retrieval, The InternetTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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13Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.detection | 1, 113 |
| abstract_inverted_index.framework | 108 |
| abstract_inverted_index.including | 52 |
| abstract_inverted_index.leverages | 39 |
| abstract_inverted_index.malicious | 10 |
| abstract_inverted_index.security. | 15 |
| abstract_inverted_index.six-layer | 42 |
| abstract_inverted_index.activities | 11 |
| abstract_inverted_index.attention, | 54 |
| abstract_inverted_index.compromise | 13 |
| abstract_inverted_index.contextual | 47 |
| abstract_inverted_index.detection. | 31 |
| abstract_inverted_index.realities, | 8 |
| abstract_inverted_index.Experiments | 89 |
| abstract_inverted_index.classifier, | 53 |
| abstract_inverted_index.demonstrate | 90 |
| abstract_inverted_index.embeddings. | 48 |
| abstract_inverted_index.integrating | 22 |
| abstract_inverted_index.Technologies | 27, 66 |
| abstract_inverted_index.concatenated | 79 |
| abstract_inverted_index.performance, | 92 |
| abstract_inverted_index.preprocesses | 35 |
| abstract_inverted_index.environments. | 117 |
| abstract_inverted_index.outperforming | 102 |
| abstract_inverted_index.classification. | 88 |
| abstract_inverted_index.representation. | 77 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.98418155 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |