Log Message Anomaly Detection using Positive and Unlabeled Learning

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dc.contributor.authorSeifishahpar, Fatemeh
dc.contributor.supervisorGulliver, T. Aaron
dc.date.accessioned2024-01-30T00:22:58Z
dc.date.available2024-01-30T00:22:58Z
dc.date.copyright2024en_US
dc.date.issued2024-01-29
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractLog messages are widely used in cloud servers and software systems. Anomaly detection of log messages is important as millions of logs are generated each day. However, besides having a complex and unstructured form, log messages are large unlabeled datasets which makes classification very difficult. In this thesis, a log message anomaly detection technique is proposed which employs Positive and Unlabeled Learning (PU Learning) to detect anomalies. Aggregated reliable negative logs are selected using the Isolation Forest, PU Learning, and Random Forest algorithms. Then, anomaly detection is conducted using deep learning Long Short-Term Memory (LSTM) network. The proposed model is evaluated using the commonly employed Openstack, BGL, and Thunderbird datasets and the results obtained indicate that the proposed model performs better than several well-known approaches in the literature.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/15909
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectAnomaly detectionen_US
dc.subjectClassificationen_US
dc.subjectDeep learningen_US
dc.subjectLog messagesen_US
dc.subjectPU learningen_US
dc.titleLog Message Anomaly Detection using Positive and Unlabeled Learningen_US
dc.typeThesisen_US

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