Details
| DOI: | 10.3390/s21134319 |
|---|---|
| Publication type: | Article |
| Journal: | Sensors |
| Publisher: | Institute of Electrical and Electronics Engineers |
| Publication date: | 2021-06-24 |
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.
Authors
- Maria-Elena Mihailescu
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University Politehnica of Bucharest
Bucharest, Romania - Darius Mihai
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University Politehnica of Bucharest
Bucharest, Romania - Mihai Carabas
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RoEduNet
Bucharest, Romania - Mikołaj Komisarek
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ITTI Sp. z o.o. | UTP University of Science and Technology
Poznań, Poland | Bydgoszcz, Poland - Marek Pawlicki
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ITTI Sp. z o.o.
Poznań, Poland - Witold Hołubowicz
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UTP University of Science and Technology<
Bydgoszcz, Poland - Rafał Kozik
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ITTI Sp. z o.o.
Poznań, Poland