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XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization
Research article (Journal of Information Security and Applications, 2021) · cited 39× · AI/ML
XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization
Summary
XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization is a scholarly article[1].
Key Facts
XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization. Retrieved May 24, 2026, from https://4ort.xyz/entity/xgbxss-an-extreme-gradient-boosting-detection-framework-for-cross-site-scripting-attacks-based-on-hybrid-feature-selecti
MLA“XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/xgbxss-an-extreme-gradient-boosting-detection-framework-for-cross-site-scripting-attacks-based-on-hybrid-feature-selecti.
BibTeX@misc{4ortxyz_xgbxss-an-extreme-gradient-boosting-detection-framework-for-cross-site-scripting-attacks-based-on-hybrid-feature-selecti_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization}}, year = {2026}, url = {https://4ort.xyz/entity/xgbxss-an-extreme-gradient-boosting-detection-framework-for-cross-site-scripting-attacks-based-on-hybrid-feature-selecti}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization — https://4ort.xyz/entity/xgbxss-an-extreme-gradient-boosting-detection-framework-for-cross-site-scripting-attacks-based-on-hybrid-feature-selecti (retrieved 2026-05-24)
