Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78349
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dc.contributor.authorGoh, Jun Wei
dc.date.accessioned2019-06-18T09:19:33Z
dc.date.available2019-06-18T09:19:33Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/78349
dc.description.abstractOrganisations often place utmost concerns to deter external cyber threats from the network perimeter. However, there has been a growing concern to detect insider threats as it can also cause adverse effects to the organisational assets such as theft of intellectual property and sabotage of network systems. Insiders are people who have certain level of privileged access to the organisation’s system which allowed them to exfiltrate confidential data for personal gain. Hence, this makes detecting insider threats challenging as their activities may mimic legitimate users’ actions. Nevertheless, an early detection of malicious activities can help organisation to mitigate potential loss of organisational asset. This project attempt to study the application of a statistical model, logistic regression to detect insider threats.en_US
dc.format.extent47 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleDesign security schemes against insider attacksen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorMa Maodeen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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