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HTTP Application Anomaly Detection
Rádsetoulal, Vlastimil ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
The goal of this work is to introduce anomaly detection principles and review its possibilities, as one of the intrusion detection methods in HTTP traffic. This work contains theoretical background crucial for performing an anomaly detection on HTTP traffic, and for utilising neural networks in achieving this goal. The work proposes tailored design of an anomaly detection model for concrete web server implementation, describes its implementation and evaluates the results. The result of this work is successful initial experiment, of modeling normal behavior of HTTP traffic and creation of the mechanism, capable of detection of anomalies within future traffic.
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Applications of Machine Learning in Predictive Maintenance of Industry 4.0
Navrátil, Tadeáš ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
The thesis develops machine learning algorithms for use in the Industry 4.0 concept. The main focus is on predictive maintenance and visual inspection. In the theoretical part, the thesis focuses on a literature search of machine learning methods in the field of anomaly detection in time series and image data. The practical part deals with the reimplementation of the selected methods and their evaluation using the confusion matrix and metrics based on it
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HTTP Application Anomaly Detection
Rádsetoulal, Vlastimil ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
The goal of this work is to introduce anomaly detection principles and review its possibilities, as one of the intrusion detection methods in HTTP traffic. This work contains theoretical background crucial for performing an anomaly detection on HTTP traffic, and for utilising neural networks in achieving this goal. The work proposes tailored design of an anomaly detection model for concrete web server implementation, describes its implementation and evaluates the results. The result of this work is successful initial experiment, of modeling normal behavior of HTTP traffic and creation of the mechanism, capable of detection of anomalies within future traffic.
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