Single Flow Time Series Datasets

Overview

This collection of datasets [1] was created from 15 well-known published datasets covering the most important traffic detection (binary) and classification (multiclass) tasks. Each dataset contains 69 features extracted through Time Series Analysis of Single Flow Time Series.

Dataset Metadata

Supported Tasks

The datasets support the following network traffic analysis tasks:

• Botnet detection and classification
• Cryptomining detection
• DNS malware detection
• DNS over HTTPS (DoH) detection
• DoS attack detection
• HTTPS Bruteforce detection
• Intrusion detection and classification
• IoT malware classification
• TOR traffic detection and classification
• VPN traffic detection and classification

Methodology

Single Flow Time Series

The Single Flow Time Series is a time series of packet payloads within a flow, consisting of payload sizes (in bytes) with corresponding transmission timestamps. This approach enables detailed analysis of flow-level behavior patterns.

Feature Extraction

Each dataset contains 69 features derived from Time Series Analysis applied to single flow time series data. These features capture temporal patterns, statistical properties, and behavioral characteristics of network flows.

How to Cite

@inproceedings{koumar2023network,
  title={Network traffic classification based on single flow time series
  analysis},
  author={Koumar, Josef and Hynek, Karel and {\v{C}}ejka,
  Tom{\'a}{\v{s}}},
  booktitle={2023 19th International Conference on Network and Service
  Management (CNSM)},
  pages={1--7},
  year={2023},
  organization={IEEE}
  }

Download

[1] Josef Koumar, Karel Hynek, & Tomáš Čejka. (2023). Network Traffic Datasets Created by Single Flow Time Series Analysis [Data set]. Zenodo.
DOI: 10.5281/zenodo.8035724