The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset

Department of Computer Science and Engineering, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, 060042 Bucharest, Romania

RoEduNet, Strada Mendeleev 21-25, 010362 Bucharest, Romania

ITTI Sp. z o.o., ul. Rubież 46, 61-612 Poznań, Poland

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Institute of Telecommunications and Computer Science, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland

Author to whom correspondence should be addressed.

Academic Editor: Wai Lok Woo

Sensors 2021, 21(13), 4319; https://doi.org/10.3390/s21134319

Received: 24 May 2021 / Revised: 17 June 2021 / Accepted: 18 June 2021 / Published: 24 June 2021

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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Abstract

Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant need for novel, realistic cybersecurity datasets. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture. The main contribution of this work is a dataset coming from a real-world, academic network. Real-life traffic was collected and, after performing a series of attacks, a dataset was assembled. The dataset contains 44 network features and an unbalanced distribution of classes. In this work, the capability of the dataset for formulating machine-learning-based models was experimentally evaluated. To investigate the stability of the obtained models, cross-validation was performed, and an array of detection metrics were reported. The gathered dataset is part of an effort to bring security against novel cyberthreats and was completed in the SIMARGL project. View Full-Text

Keywords: machine learning; network intrusion detection; dataset

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MDPI and ACS Style

Mihailescu, M.-E.; Mihai, D.; Carabas, M.; Komisarek, M.; Pawlicki, M.; Hołubowicz, W.; Kozik, R. The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset. Sensors 2021, 21, 4319. https://doi.org/10.3390/s21134319

AMA Style

Mihailescu M-E, Mihai D, Carabas M, Komisarek M, Pawlicki M, Hołubowicz W, Kozik R. The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset. Sensors. 2021; 21(13):4319. https://doi.org/10.3390/s21134319

Chicago/Turabian Style

Mihailescu, Maria-Elena, Darius Mihai, Mihai Carabas, Mikołaj Komisarek, Marek Pawlicki, Witold Hołubowicz, and Rafał Kozik. 2021. "The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset" Sensors 21, no. 13: 4319. https://doi.org/10.3390/s21134319

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