Szczegóły publikacji
Opis bibliograficzny
Utilizing TabNet deep learning for elephant flow detection by analyzing information in first packet headers / Bartosz KĄDZIOŁKA, Piotr JURKIEWICZ, Robert WÓJCIK, Jerzy DOMŻAŁ // Entropy [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1099-4300. — 2024 — vol. 26 iss. 7 art. no. 537, s. 1-17. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 16-17, Abstr. — Publikacja dostępna online od: 2024-06-22
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 154827 |
|---|---|
| Data dodania do BaDAP | 2024-08-14 |
| Tekst źródłowy | URL |
| DOI | 10.3390/e26070537 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Entropy |
Abstract
Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries. The model was trained and tested on a comprehensive dataset from a campus network, demonstrating its robustness and potential applicability to varied network environments.
