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Detecting Spammer in Email Social Networks

Show simple item record Adam, Salma Elnageeb Supervisor - iMohamed Alhafiz Mustafa 2014-06-08T11:51:15Z 2014-06-08T11:51:15Z 2012-02-28
dc.identifier.citation Adam,Salma Elnageeb.Detecting Spammer in Email Social Networks Case Study: Email of Sudan University of Science and Technology/ Salma Elnageeb Adam؛ Mohamed Alhafiz Mustafa .-Khartoum : sudan university of science and technology, computer science,2012.-93p:ill;28cm;M.Sc. en_US
dc.description Thesis en_US
dc.description.abstract The massive increase of spam is posing a very serious threat to email which has become an important means for communication. Not only it annoys users, but it also consumes much of the bandwidth of the Internet. Current spam filters are based on the contents of the email one way or the other. In this thesis we present a social network-based spam detection method in which the core idea is using social network measurements as feature to be used by classifier. Two separate classification models have been designed and tested. The first is k-Nearest-Neighbor Classifiers (KNN) classifier and the second is Locally weighted learning (LWL). The experimental results have shown a great favour of using KNN model for spam detection. However, it classifies many legitimate as spam which may annoy the email user. Hence we recommend this model to be applied where the acceptance of a spam message is more danger than legitimate messages rejection. While the classification result of LWL is better than KNN result. It is clear that KNN has advantage of detecting all spammer. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject Social Networks en_US
dc.subject Spammer en_US
dc.subject Email Social Networks en_US
dc.subject spam en_US
dc.title Detecting Spammer in Email Social Networks en_US
dc.title.alternative إكتشاف المرسلين المزعجين في شبكات البريد الإلكتروني الإجتماعية en_US
dc.type Thesis en_US

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