CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909327881666560 |
|---|---|
| author | Koumar, Josef Hynek, Karel Čejka, Tomáš Šiška, Pavel |
| author_facet | Koumar, Josef Hynek, Karel Čejka, Tomáš Šiška, Pavel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting Koumar, Josef Hynek, Karel Čejka, Tomáš Šiška, Pavel Machine Learning Artificial Intelligence 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. |
| title | CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.18874 |