{"title":"flow-models 2.2: Efficient and parallel elephant flow modeling with machine learning","authors":"Piotr Jurkiewicz","doi":"10.1016/j.softx.2024.101920","DOIUrl":null,"url":null,"abstract":"<div><div>This article introduces the latest version of the <span>flow-models</span> 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.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101920"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024002905","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
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