# Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2503.04404 · HTML: https://mlsanity.com/paper/2503.04404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 81 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [A Survey of Network-based Intrusion Detection Data Sets](https://mlsanity.com/paper/1903.02460/) (2019)
- [NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2011.09144/) (2020)
- [Towards a Standard Feature Set for Network Intrusion Detection System Datasets](https://mlsanity.com/paper/2101.11315/) (2021)
- [FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2304.14746/) (2023)
- [Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives](https://mlsanity.com/paper/1811.05372/) (2018)
- [SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2305.00550/) (2023)
- [Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation](https://mlsanity.com/paper/2011.03381/) (2020)
- [Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection](https://mlsanity.com/paper/2104.07183/) (2021)
- [The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2203.04686/) (2022)
- [Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data](https://mlsanity.com/paper/1809.07500/) (2018)
- [Exploring Edge TPU for Network Intrusion Detection in IoT](https://mlsanity.com/paper/2103.16295/) (2021)
- [Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2108.12722/) (2021)
- [Flow length and size distributions in campus Internet traffic](https://mlsanity.com/paper/1809.03486/) (2018)
- [A Review of Computer Vision Methods in Network Security](https://mlsanity.com/paper/2005.03318/) (2020)
- [On the Evaluation of Sequential Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2106.07961/) (2021)
- ["Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law](https://mlsanity.com/paper/1609.04214/) (2016)

## Built on by (most cited first, 2 of 2)

- [Self-Supervised Learning of Graph Representations for Network Intrusion Detection](https://mlsanity.com/paper/2509.16625/) (2025)
- [MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining](https://mlsanity.com/paper/2605.11034/) (2026)

## 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.