Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

Researchers led by Siamak Layeghy released a set of NetFlow datasets incorporating temporal features for machine learning-based network intrusion detection systems (NIDS), addressing a gap in existing datasets. Their temporal analysis, including time-frequency signal presentations, revealed that many attacks have unique patterns that could help ML models identify them more easily.

Computer Science Machine Learning Submitted on 6 Mar 2025 v1 https://arxiv.org/abs/2503.04404v1 , last revised 28 Aug 2026 this version, v3 Title:Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems View PDF /pdf/2503.04404 HTML experimental https://arxiv.org/html/2503.04404v3 Abstract:This paper investigates the temporal analysis of NetFlow datasets for machine learning ML -based network intrusion detection systems NIDS . Although many previous studies have highlighted the critical role of temporal features, such as inter-packet arrival time and flow length/duration, in NIDS, the currently available NetFlow datasets for NIDS lack these temporal features. This study addresses this gap by creating and making publicly available a set of NetFlow datasets that incorporate these temporal features 1 . With these temporal features, we provide a comprehensive temporal analysis of NetFlow datasets by examining the distribution of various features over time and presenting time-series representations of NetFlow features. This temporal analysis has not been previously provided in the existing literature. We also borrowed an idea from signal processing, time frequency analysis, and tested it to see how different the time frequency signal presentations TFSPs are for various attacks. The results indicate that many attacks have unique patterns, which could help ML models to identify them more easily. Submission history From: Siamak Layeghy view email /show-email/80778c70/2503.04404 Thu, 6 Mar 2025 12:58:09 UTC 1,206 KB v1 /abs/2503.04404v1 Sun, 9 Mar 2025 07:31:18 UTC 781 KB v2 /abs/2503.04404v2 v3 Fri, 28 Aug 2026 23:59:38 UTC 434 KB Current browse context: cs.LG References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender IArxiv Recommender What is IArxiv? https://iarxiv.org/about arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .