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System Log Analysis for Anomaly Detection Using Machine Learning
Šiklóši, Miroslav ; Fujdiak, Radek (oponent) ; Hošek, Jiří (vedoucí práce)
This thesis deals with system log analysis for anomaly detection using machine learning models. The proposed models are based on supervised, unsupervised and deep learning algorithms. However, the functionality and behaviour of these algorithms have been clarified theoretically and practically in the thesis. Moreover, many preprocessing methods and logics were used to preprocess the data before it was fed to the machine learning model. At the end and to confirm the workability of proposed models, many metrics were calculated and unseen syslogs were fed to the best-proposed machine learning models to detect the anomalies. However, models Decision Tree Classifier, One-class SVM and Hierarchical Clustering demonstrated the best performance, correctly predicting 93.95%,85.66% and 85.3% of all anomalies respectively.
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System Log Analysis for Anomaly Detection Using Machine Learning
Šiklóši, Miroslav ; Fujdiak, Radek (oponent) ; Hošek, Jiří (vedoucí práce)
This thesis deals with system log analysis for anomaly detection using machine learning models. The proposed models are based on supervised, unsupervised and deep learning algorithms. However, the functionality and behaviour of these algorithms have been clarified theoretically and practically in the thesis. Moreover, many preprocessing methods and logics were used to preprocess the data before it was fed to the machine learning model. At the end and to confirm the workability of proposed models, many metrics were calculated and unseen syslogs were fed to the best-proposed machine learning models to detect the anomalies. However, models Decision Tree Classifier, One-class SVM and Hierarchical Clustering demonstrated the best performance, correctly predicting 93.95%,85.66% and 85.3% of all anomalies respectively.
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