A comprehensive evaluation of machine learning algorithms for web application attack detection with knowledge graph integration
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Integrating knowledge graphs improves explainability, helping to identify and understand potential threats more effectively.
“Furthermore, integrating knowledge graphs improves explainability, helping to identify and understand potential threats more effectively [108].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [108]
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References 89 · Cited by 12
- Authors
- Ismail, M., Alrabaee, S., Choo, K.-K. R., Ali, L., & Harous, S.
- Affiliations
- United Arab Emirates University (AE)
- The University of Texas at San Antonio (US)
- University of Sharjah (AE)
- Corresponding author
- Ismail, Muhusina
- Alrabaee, Saed
- Choo, Kim-Kwang Raymond
- Ali, Luqman
- Harous, Saad
- Venue
- Mobile Networks and Applications
- Published
- 2024-06
- Volume
- 29
- Issue
- 3
- Pages
- 1008-1037
- ISSN
- 1383-469X (ISSN-L)
- 1572-8153
- OpenAlex
- W4400836341
- Cited by
- 12
- References
- 89
- Citations by year
- 2026: 3 · 2025: 9
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Mobile Networks and Applications (publishedVersion)
- Funders
- United Arab Emirates University (12R170)
- Topics
- Physical Sciences › Computer Science › Information Systems › Web Application Security Vulnerabilities
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- web application attack detection, knowledge graph integration, multilayer perceptron, ensemble methods, Gaussian processes, k-nearest neighbors, classifier comparison, web security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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