| DC Field | Value | Language |
|---|
| dc.contributor.author | Pan Huiqin | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2026-05-20T08:41:13Z | - |
| dc.date.available | 2026-05-20T08:41:13Z | - |
| dc.date.issued | 2026 | - |
| dc.identifier.citation | Pan Huiqin. A Machine Learning Framework for Multiclass Detection of DDoS Network Attacks / Pan Huiqin // Информационная безопасность : сборник материалов 62-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 13–17 апреля 2026 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: С. В. Дробот (гл. ред.) [и др.]. – Минск, 2026. – С. 38–39. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/63765 | - |
| dc.description.abstract | This paper studies machine-learning-based multiclass detection of DDoS traffic using the CIC-DDoS2019 dataset. After preprocessing, four models are compared: SVM, Random Forest, XGBoost, and MLP. The results show that Random Forest achieves the best overall performance, with near-perfect accuracy and macro-F1. A practical deployment scheme at network boundaries is also outlined for near real-time detection. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | DDoS-attacks | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | supervised learning | en_US |
| dc.title | A Machine Learning Framework for Multiclass Detection of DDoS Network Attacks | en_US |
| Appears in Collections: | Информационная безопасность : материалы 62-й научной конференции аспирантов, магистрантов и студентов (2026)
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