Bc. Júlia Ščensná

Master's thesis

Supervised and Unsupervised Machine Learning Methods for System Log Anomaly Detection

Supervised and Unsupervised Machine Learning Methods for System Log Anomaly Detection
Abstract:
Táto práca sa zaoberá implementáciou sady nástrojov, ktoré využívajú algoritmy strojového učenia pre klasifikáciu systémových logov a detekovanie anomálií. Navrhované modely sú postavené na dvoch kategóriách strojového učenia, a to učenie s učiteľom (supervised learning) a učenie bez učiteľa (unsupervised learning). V rámci práce je funkcionalita a správanie vybraných metód vysvetlená teoreticky aj …more
Abstract:
This thesis deals with implementing toolset that uses machine learning algorithms for system logs classification and anomaly detection. The proposed models are built based on supervised and unsupervised machine learning methods. However, the functionality and behavior of these methods have been explained theoretically and practically in the thesis. Sufficient numbers of simulated plots are included …more
 
 
Language used: English
Date on which the thesis was submitted / produced: 19. 5. 2020

Thesis defence

  • Date of defence: 15. 6. 2020
  • Supervisor: RNDr. Radek Ošlejšek, Ph.D.
  • Reader: doc. RNDr. Ivan Kopeček, CSc.

Citation record

Full text of thesis

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Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatiky

Masaryk University

Faculty of Informatics

Master programme / field:
Applied Informatics / Applied Informatics