Models to Combat Email Spam Botnets and Unwanted Phone Calls

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With the amount of email spam received these days it is hard to imagine that spammers act individually. Nowadays, most of the spam emails have been sent from a collection of compromised machines controlled by some spammers. These compromised computers are often called bots, using which the spammers can send massive volume of spam within a short period of time. The motivation of this work is to understand and analyze the behavior of spammers through a large collection of spam mails. My research examined a the data set collected over a 2.5-year period and developed an algorithm which would give ... continued below

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Husna, Husain May 2008.

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  • Husna, Husain

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With the amount of email spam received these days it is hard to imagine that spammers act individually. Nowadays, most of the spam emails have been sent from a collection of compromised machines controlled by some spammers. These compromised computers are often called bots, using which the spammers can send massive volume of spam within a short period of time. The motivation of this work is to understand and analyze the behavior of spammers through a large collection of spam mails. My research examined a the data set collected over a 2.5-year period and developed an algorithm which would give the botnet features and then classify them into various groups. Principal component analysis was used to study the association patterns of group of spammers and the individual behavior of a spammer in a given domain. This is based on the features which capture maximum variance of information we have clustered. Presence information is a growing tool towards more efficient communication and providing new services and features within a business setting and much more. The main contribution in my thesis is to propose the willingness estimator that can estimate the callee's willingness without his/her involvement, the model estimates willingness level based on call history. Finally, the accuracy of the proposed willingness estimator is validated with the actual call logs.

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  • May 2008

Added to The UNT Digital Library

  • Oct. 2, 2008, 4:43 p.m.

Description Last Updated

  • Oct. 20, 2008, 3:36 p.m.

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Husna, Husain. Models to Combat Email Spam Botnets and Unwanted Phone Calls, thesis, May 2008; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc6095/: accessed October 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .