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 | ||
dc.identifier.uri | ||
dc.subject | ||
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 | ||
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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