M. E. Rafiq, A. Newaz and Marsono, M. N. and Gebali, F. (2007) On the effects of de-obfuscation on spam detection accuracy. In: Advances In Digital Signal Processing Applications. Penerbit UTM , Johor, pp. 159-172. ISBN 978-983-52-0652-8
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Spam contributes to approximately two-thirds of the e-mail traffic over the Internet  and is fast becoming a major problem for IT users and network administrators. Spam costs billions in lost productivity  and results in more problems than mere annoyance of delayed and lost non-spam emails. Naive Bayes classification has widely been used for spam detection and several variations have been proposed , , . In e-mail content classification (as other supervised-learning techniques), the accuracy (of spam detection) depends on the frequency of spam features observed during training. Spam continuously evolves to circumvent systems and is becoming much more sophisticated . Spammers obfuscate wellknown spam features in different ways to circumvent spam detection . Obfuscating spam features (even by substituting a character with a visually similar one) reduces the frequency and size of features observed during learning. Hence, if obfuscated spam features can be de-obfuscated first before the detection, then the accuracy of spam detection would increase. This statement is proved in this chapter by experimenting with real spam e-mails.
|Item Type:||Book Section|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Deposited By:||Liza Porijo|
|Deposited On:||15 Aug 2011 05:41|
|Last Modified:||15 Aug 2011 05:41|
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