Szczegóły publikacji
Opis bibliograficzny
Flow-models 2.0: elephant flows modeling and detection with machine learning : software update / Piotr JURKIEWICZ // SoftwareX [Dokument elektroniczny]. - Czasopismo elektroniczne ; ISSN 2352-7110. — 2023 — vol. 24 art. no. 101506, s. 1–5. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 4–5, Abstr. — Publikacja dostępna online od: 2023-09-21. --- Refers to: Piotr Jurkiewicz, flow-models: A framework for analysis and modeling of IP network flows, SoftwareX, Volume 17, 2022, 100929, ISSN 2352-7110, 10.1016/j.softx.2021.100929.
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151099 |
|---|---|
| Data dodania do BaDAP | 2024-01-12 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.softx.2023.101506 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | SoftwareX |
Abstract
This article presents the new version of the flow-models IP network flow modeling framework. The improved features include flow skipping and counting, flow filtering, IP address anonymization, and time series data calculation. The new version also enables simulation of the first packet mirroring feature and provides tools for modeling the detection of elephant flows. It includes examples of using the scikit-learn library to build machine learning models for elephant flow detection based on the first packet. Furthermore, it provides an anonymized flow dataset, enabling researchers to train and validate machine learning models for traffic analysis in a reproducible and comparable manner.
