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Facebook Account Misuse Detection -- A Statistical Approach

dc.contributor.author Chai, Christine
dc.contributor.editor Lei, CL
dc.date.accessioned 2017-05-06T00:33:08Z
dc.date.available 2017-05-06T00:33:08Z
dc.date.issued 2013-06-30
dc.identifier https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxjaHJpc3RpbmVwZWlqaW5uY2hhaXxneDoyYTk2OWI1ZGI4OWFjMmEw
dc.identifier.uri http://hdl.handle.net/10161/14285
dc.description.abstract Privacy of personal information on social networking websites has become an important issue, because when a social networking website account is used by a person other than the owner, all personal data stored on the website can be retrieved, no matter how the owner sets the privacy options. Therefore, this paper proposes a statistical approach with the use of Support Vector Machine (SVM) to detect whether the Facebook account user is the actual owner. By analyzing online browsing behavior features, it is found that the normal user tends to be more active and that the stealthy user prefers to read personal messages.
dc.subject Facebook
dc.subject account misuse
dc.subject statistical approach
dc.subject Support Vector Machine (SVM)
dc.subject classification
dc.subject cross validation
dc.title Facebook Account Misuse Detection -- A Statistical Approach
dc.type Other article
pubs.author-url https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxjaHJpc3RpbmVwZWlqaW5uY2hhaXxneDoyYTk2OWI1ZGI4OWFjMmEw
pubs.organisational-group Duke
pubs.organisational-group Statistical Science
pubs.organisational-group Student
pubs.organisational-group Trinity College of Arts & Sciences


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