Assessing IP Weight Metrics for Cloud Intrusion Detection using Machine Learning Techniques

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dc.contributor.author Hu, Ruiqi
dc.date.accessioned 2018-02-22T18:38:54Z
dc.date.available 2018-02-22T18:38:54Z
dc.date.copyright 2018 en_US
dc.date.issued 2018-02-22
dc.identifier.uri http://hdl.handle.net/1828/9088
dc.description.abstract Despite the growing popularity of cloud computing, security is still an important concern of cloud customers and potential adopters. Cloud computing is prone to the same attack vectors as traditional networks, in addition to new attack vectors that are specific to cloud platforms. Intrusion Detection Systems (IDS) deployed in the cloud must take into account the specificity of the underlying threat landscape as well as the architectural and operational constraints of cloud platforms. In this project, an IDS that utilizes IP weight metrics for feature selection is implemented. Additionally, this system is tested with different supervised classification models and evaluated on a cloud intrusion dataset. In comparison with the results under conventional network environment, we conclude that the performance of IDS against cloud intrusions is promising, however, other developments such as unsupervised intrusion detection techniques and extra data preprocessing stages should be researched for the best practice of the system. en_US
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Intrusion Detection en_US
dc.subject IP Weight Metrics en_US
dc.subject Machine Learning en_US
dc.title Assessing IP Weight Metrics for Cloud Intrusion Detection using Machine Learning Techniques en_US
dc.type project en_US
dc.contributor.supervisor Traoré, Issa
dc.degree.department Department of Electrical and Computer Engineering en_US
dc.degree.level Master of Engineering M.Eng. en_US
dc.description.scholarlevel Graduate en_US

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