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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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Anomaly Detection in Generated Incident Ticket Volumes
Šurina, Timotej ; Rychlý, Marek (referee) ; Trchalík, Roman (advisor)
Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.
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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.
|
|
|
Anomaly Detection in Generated Incident Ticket Volumes
Šurina, Timotej ; Rychlý, Marek (referee) ; Trchalík, Roman (advisor)
Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.
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