Facebook Account Misuse Detection -- A Statistical Approach

dc.contributor.author

Chai, CP

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.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.identifier

https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxjaHJpc3RpbmVwZWlqaW5uY2hhaXxneDoyYTk2OWI1ZGI4OWFjMmEw

dc.identifier.uri

https://hdl.handle.net/10161/14285

dc.subject

Facebook

dc.subject

account misuse

dc.subject

statistical approach

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Support Vector Machine (SVM)

dc.subject

classification

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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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