Investigating keystroke dynamics as a two-factor biometric security

Simple item page
dc.contributor.authorNjogholo, Brian Mwandau
dc.date.accessioned2018-10-23T08:50:21Z
dc.date.available2018-10-23T08:50:21Z
dc.date.issued2018
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Systems Security (MSc.ISS) at Strathmore Universityen_US
dc.description.abstractKeystroke dynamics is the study of how people can be distinguished based on their typing rhythms. This proposal aims at investigating user authentication approaches and how keystroke dynamics can be used to enhance user authentication and access control. With more users embracing technologies and using applications without necessarily understanding the security repercussions, a further protection mechanism needs to be employed. It emphasizes on the need of an additional layer of security, through keystroke dynamics, on top of the traditional username-password combination to enhance security during authentication. It also proposes the use of a machine learning classifier for possible application in keystroke dynamics to verify and validate the legitimacy of a user during authentication.en_US
dc.identifier.urihttp://hdl.handle.net/11071/5991
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectValidation.en_US
dc.subjectKeystroke Dynamicsen_US
dc.subjectAuthenticationen_US
dc.subjectMachine Learningen_US
dc.subjectArtificial Neural Networken_US
dc.subjectBiometricen_US
dc.subjectSecurityen_US
dc.titleInvestigating keystroke dynamics as a two-factor biometric securityen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Investigating keystroke dynamics as a two-factor biometric security.pdf
Size:
7.89 MB
Format:
Adobe Portable Document Format
Description:
Full-text Thesis 2018
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
MSc.ISS Theses and Dissertations (2018)