DOI: 10.5281/zenodo.8035724
title: Network traffic datasets created by Single Flow Time Series Analysis

description: Network traffic datasets created by Single Flow Time Series Analysis

Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as: 



J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.


This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

 

In the following table is a description of each dataset file:




File name
Detection problem
Citation of original raw dataset


botnet_binary.csv 
Binary detection of botnet 
S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. 


botnet_multiclass.csv 
Multi-class classification of botnet 
S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. 


cryptomining_design.csv
Binary detection of cryptomining; the design part 
Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 


cryptomining_evaluation.csv 
Binary detection of cryptomining; the evaluation part 
Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 


dns_malware.csv 
Binary detection of malware DNS 
Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. 


doh_cic.csv 
Binary detection of DoH 


Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 



doh_real_world.csv 
Binary detection of DoH 
Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 


dos.csv 
Binary detection of DoS 
Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.


edge_iiot_binary.csv 
Binary detection of IoT malware 
Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.


edge_iiot_multiclass.csv
Multi-class classification of IoT malware
Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.


https_brute_force.csv
Binary detection of HTTPS Brute Force
Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020


ids_cic_binary.csv
Binary detection of intrusion in IDS
Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.


ids_cic_multiclass.csv 
Multi-class classification of intrusion in IDS 
Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. 


ids_unsw_nb_15_binary.csv 
Binary detection of intrusion in IDS 
Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.


ids_unsw_nb_15_multiclass.csv 
Multi-class classification of intrusion in IDS 
Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.


iot_23.csv 
Binary detection of IoT malware 
Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23


ton_iot_binary.csv 
Binary detection of IoT malware 
Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021


ton_iot_multiclass.csv 
Multi-class classification of IoT malware 
Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021


tor_binary.csv 
Binary detection of TOR 
Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. 


tor_multiclass.csv 
Multi-class classification of TOR 
Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. 


vpn_iscx_binary.csv 
Binary detection of VPN 
Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. 


vpn_iscx_multiclass.csv 
Multi-class classification of VPN 
Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. 


vpn_vnat_binary.csv 
Binary detection of VPN 
Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022


vpn_vnat_multiclass.csv
Multi-class classification of VPN 
Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022




 
description: This research was funded by the Ministry of Interior of the Czech Republic, grant No. VJ02010024: Flow-Based Encrypted Traffic Analysis and also by the Grant Agency of the CTU in Prague, grant No. SGS23/207/OHK3/3T/18 funded by the MEYS of the Czech Republic.