flow-models 2.2：利用机器学习进行高效并行的象流建模

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2024-10-19 DOI:10.1016/j.softx.2024.101920

Piotr Jurkiewicz

{"title":"flow-models 2.2：利用机器学习进行高效并行的象流建模","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":"{\"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}","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

摘要

本文介绍了用于 IP 网络流量分析的最新版流量模型框架。主要改进包括支持 Dask 以实现并行计算、采用数据集缩减技术以实现高效训练，以及用于熵分析和粒度流表模拟的新模块。此外，还完善了代码库，改进了文档，并通过 ruff 实现了自动测试。该框架现在与即将发布的 Python 和 NumPy 兼容，使其成为从事网络流量分析和机器学习驱动的流量分类的研究人员和专业人员的有用资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

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.