mlsanityData to 7 Oct 2026

Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

arXiv 2503.04404 ↗First posted 6 Mar 2025cs.LG cs.CR cs.NIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 81 references that are in the map. First posted on arXiv 6 Mar 2025.

Builds on

19 of 81 references.

References · 19

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7 more
  1. 2018Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Datacited by 6
  2. 2021Exploring Edge TPU for Network Intrusion Detection in IoTcited by 5
  3. 2021Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networkscited by 5
  4. 2018Flow length and size distributions in campus Internet trafficcited by 4
  5. 2020A Review of Computer Vision Methods in Network Securitycited by 3
  6. 2021On the Evaluation of Sequential Machine Learning for Network Intrusion Detectioncited by 3
  7. 2016"Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Lawcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Self-Supervised Learning of Graph Representations for Network Intrusion Detectioncited by 2
  2. 2026MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretrainingcited by 0