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VHS-22 – A Very Heterogeneous Set of Network Traffic Data for Threat Detection

Authors:

Pawel Szumelda,

Natan Orzechowski,

Mariusz Rawski,

Artur JanickiAuthors Info & Claims

EICC '22: Proceedings of the 2022 European Interdisciplinary Cybersecurity Conference

Pages 72 - 78

https://doi.org/10.1145/3528580.3532843

Published: 21 July 2022 Publication History

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Abstract

Researching new methods of detecting network threats, e.g., malware-related, requires large and diverse sets of data. In recent years, a variety of network traffic datasets have been proposed, which have been intensively used by the research community. However, most of them are quite homogeneous, which means that detecting threats using these data became relatively easy, allowing for detection accuracy close to 100%. Therefore, they are not a challenge anymore. As a remedy, in this article we propose a VHS-22 dataset – a Very Heterogeneous Set of network traffic data. We prepared it using a software network probe and a set of existing datasets. We describe the process of dataset creation, as well as its basic statistics. We also present initial experiments on attack detection, which yielded lower results than for other datasets. We claim that the data in the VHS-22 dataset are more demanding, and therefore that our dataset can better stimulate further progress in detecting network threats.

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Cited By

Kabla AThamrin AAnbar MManickam SKaruppayah S(2024)Peer-to-peer botnets: exploring behavioural characteristics and machine/deep learning-based detectionEURASIP Journal on Information Security10.1186/s13635-024-00169-02024:1Online publication date: 27-May-2024
https://doi.org/10.1186/s13635-024-00169-0
Yang ZQu HHua YZhang XLin X(2024)Adversarial Attacks on Network Intrusion Detection Systems Based on Federated LearningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5606-3_13(146-157)Online publication date: 30-Jul-2024
https://doi.org/10.1007/978-981-97-5606-3_13
Komisarek MPawlicki MSimic TKavcnik DKozik RChoraś M(2023)Modern NetFlow network dataset with labeled attacks and detection methodsProceedings of the 18th International Conference on Availability, Reliability and Security10.1145/3600160.3605094(1-8)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1145/3600160.3605094
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VHS-22 – A Very Heterogeneous Set of Network Traffic Data for Threat Detection
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Published In

cover image ACM Other conferences

EICC '22: Proceedings of the 2022 European Interdisciplinary Cybersecurity Conference

June 2022

114 pages

ISBN:9781450396035

DOI:10.1145/3528580

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 21 July 2022

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EICC 2022

EICC 2022: European Interdisciplinary Cybersecurity Conference

June 15 - 16, 2022

Barcelona, Spain

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Cited By

Kabla AThamrin AAnbar MManickam SKaruppayah S(2024)Peer-to-peer botnets: exploring behavioural characteristics and machine/deep learning-based detectionEURASIP Journal on Information Security10.1186/s13635-024-00169-02024:1Online publication date: 27-May-2024
https://doi.org/10.1186/s13635-024-00169-0
Yang ZQu HHua YZhang XLin X(2024)Adversarial Attacks on Network Intrusion Detection Systems Based on Federated LearningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5606-3_13(146-157)Online publication date: 30-Jul-2024
https://doi.org/10.1007/978-981-97-5606-3_13
Komisarek MPawlicki MSimic TKavcnik DKozik RChoraś M(2023)Modern NetFlow network dataset with labeled attacks and detection methodsProceedings of the 18th International Conference on Availability, Reliability and Security10.1145/3600160.3605094(1-8)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1145/3600160.3605094
Pawlicki MPawlicka AKozik RChoraś M(2023)How to Boost Machine Learning Network Intrusion Detection Performance with Encoding SchemesComputer Information Systems and Industrial Management10.1007/978-3-031-42823-4_21(283-297)Online publication date: 15-Sep-2023
https://doi.org/10.1007/978-3-031-42823-4_21
Korona MSzumełda PRawski MJanicki A(2022)Comparison of Hash Functions for Network Traffic Acquisition Using a Hardware-Accelerated ProbeElectronics10.3390/electronics1111168811:11(1688)Online publication date: 25-May-2022
https://doi.org/10.3390/electronics11111688
Rahman FZunaied Tanna RHabiba UShaikh RRahman ZImtiaz H(2022)Cyber Threat Detection Using Machine Learning Algorithms on Heterogeneous MiniVHS-22 Dataset2022 25th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT57492.2022.10055036(866-869)Online publication date: 17-Dec-2022
https://doi.org/10.1109/ICCIT57492.2022.10055036

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