Network traffic datasets created by Single Flow Time Series Analysis

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