Work

flow-models 2.2: Efficient and parallel elephant flow modeling with machine learning

Year: 2024

Type: software-paper

Abstract: This article introduces the latest version of the flow-models framework for IP network flow analysis. Key improvements include support for Dask to enable parallel computing, dataset reduction techniques for efficient training, and new modules for entropy analysis and granular flow table simulations. The codebase has been refined, with improved documentation and the incorporation of automated testing via ruff. The framework is now compatible with forthcoming releases of Python and NumPy, making it a useful resource for researchers and professionals involved in network flow analysis and machine learning-driven traffic classification.

Source: SoftwareX

DOI: 10.1016/j.softx.2024.101920

Author: Piotr Jurkiewicz

Institution: AGH University of Krakow

Language: English

Topic: Internet Traffic Analysis and Secure E-voting

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $970