# CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting

arXiv: https://arxiv.org/abs/2409.18874 · HTML: https://mlsanity.com/paper/2409.18874/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [Fine-grained TLS services classification with reject option](https://mlsanity.com/paper/2202.11984/) (2022)
- [Datasets are not Enough: Challenges in Labeling Network Traffic](https://mlsanity.com/paper/2110.05977/) (2021)
- [A Survey on Big Data for Network Traffic Monitoring and Analysis](https://mlsanity.com/paper/2003.01648/) (2020)
- [NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification](https://mlsanity.com/paper/2310.05530/) (2023)

## Built on by (most cited first, 12 of 12)

- [Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series](https://mlsanity.com/paper/2506.10412/) (2025)
- [When Simple Model Just Works: Is Network Traffic Classification in Crisis?](https://mlsanity.com/paper/2506.08655/) (2025)
- [Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting](https://mlsanity.com/paper/2503.17410/) (2025)
- [CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives](https://mlsanity.com/paper/2608.01725/) (2026)
- [Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction](https://mlsanity.com/paper/2601.02694/) (2026)
- [Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability](https://mlsanity.com/paper/2604.21930/) (2026)
- [NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts](https://mlsanity.com/paper/2607.06607/) (2026)
- [VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet](https://mlsanity.com/paper/2608.18572/) (2026)
- [BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting](https://mlsanity.com/paper/2609.33303/) (2026)
- [Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation Under Resource Constraints](https://mlsanity.com/paper/2610.05835/) (2026)
- [QiYao-I: A Manifold Based Foundation Model for Irregular Multivariate Time Series Forecasting](https://mlsanity.com/paper/2610.06936/) (2026)
- [CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection](https://mlsanity.com/paper/2610.14799/) (2026)

## Abstract (arXiv)

Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. One of the primary approaches to anomaly detection are methods based on forecasting. Nevertheless, extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing performance overestimation of anomaly detection algorithms. This manuscript addresses this gap by introducing a dataset comprising time series data of network entities' behavior, collected from the CESNET3 network. The dataset was created from 40 weeks of network traffic of 275 thousand active IP addresses. The ISP origin of the presented data ensures a high level of variability among network entities, which forms a unique and authentic challenge for forecasting and anomaly detection models. It provides valuable insights into the practical deployment of forecast-based anomaly detection approaches.