Exploratory data analysis toward cloud intrusion detection

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dc.contributor.authorMashkanova, Aigerim
dc.contributor.supervisorTraore, Issa
dc.contributor.supervisorGanti, Sudhakar
dc.date.accessioned2019-05-31T23:21:46Z
dc.date.available2019-05-31T23:21:46Z
dc.date.copyright2019en_US
dc.date.issued2019-05-31
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractCloud computing is one of the fastest growing areas of information technologies. The growth rate and the prosperity of this industry have led to important security concerns. For the Masters Project, the ISOT Cloud Intrusion Dataset (ISOT-CID) was utilized to explore cloud anomaly detection using different machine learning tech- niques. The dataset involves terabytes of data that contain various attacks and normal activities gathered in a real cloud environment. Three different supervised machine learning techniques were applied to analyze anomalous behavior in network traffic. The implemented experiments demonstrated that the Logistic Regression classifier achieves the lowest false negative rate while the Naive Bayes has achieves the highest accuracy rate.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/10909
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectcloud computingen_US
dc.subjectintrusion detectionen_US
dc.subjectdata analysisen_US
dc.titleExploratory data analysis toward cloud intrusion detectionen_US
dc.typeprojecten_US

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