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Učení se automatů pro rychlou detekci anomálií v síťovém provozu / Automata Learning for Fast Detection of Anomalies in Network Traffic

The focus of this thesis is the fast network anomaly detection based on automata learning. It describes and compares several chosen automata learning algorithms including their adaptation for the learning of network characteristics. In this work, various network anomaly detection methods based on learned automata are proposed which can detect sequential as well as statistical anomalies in target communication. For this purpose, they utilize automata's mechanisms, their transformations, and statistical analysis. Proposed detection methods were implemented and evaluated using network traffic of the protocol IEC 60870-5-104 which is commonly used in industrial control systems.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:449296
Date January 2021
CreatorsHošták, Viliam Samuel
ContributorsMatoušek, Petr, Holík, Lukáš
PublisherVysoké učení technické v Brně. Fakulta informačních technologií
Source SetsCzech ETDs
LanguageCzech
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

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