Details
| DOI: | 10.1145/3465481.3470091 |
|---|---|
| Publication type: | Conference paper |
| Conference: | ARES 2021: International Conference on Availability, Reliability and Security |
| Location: | Virtual |
| Online publication date: | 2021-08-17 |
Abstract
There is a profuse abundance of network security incidents around the world every day. Increasingly, services and data stored on servers fall victim to sophisticated techniques that cause all sorts of damage. Hackers invent new ways to bypass security measures and modify the existing viruses in order to deceive defense systems. Therefore, in response to these illegal procedures, new ways to defend against them are being developed. In this paper, a method for anomaly detection based on machine learning technique is presented and a near real-time processing system architecture is proposed. The main contribution is a test-run of ML algorithms on real-world data coming from a world-class telecom operator. This work investigates the effectiveness of detecting malicious behaviour in network packets using several machine learning techniques. The results achieved are expressed with a set of of metrics. For better clarity on the classifier performance, 10-fold cross-validation was used.
Authors
- 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. | UTP University of Science and Technology
Poznań, Poland | Bydgoszcz, Poland - Mikołaj Kowalski
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Orange Poland
Warsaw, Poland - Adrian Marzecki
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Orange Poland
Warsaw, Poland - Rafał Kozik
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ITTI Sp. z o.o. | UTP University of Science and Technology
Poznań, Poland | Bydgoszcz, Poland - Michał Choraś
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FernUniversität in Hagen | UTP University of Science and Technology
Hagen, Germany | Bydgoszcz, Poland