M. E. Rafiq, A. Newaz and Marsono, Muhammad Nadzir and Gebali, Fayez (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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Abstract
Spam contributes to approximately two-thirds of the e-mail traffic over the Internet [4] and is fast becoming a major problem for IT users and network administrators. Spam costs billions in lost productivity [13] 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 [19], [1], [5]. 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 [6]. Spammers obfuscate wellknown spam features in different ways to circumvent spam detection [12]. 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 |
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Electrical Engineering |
ID Code: | 13680 |
Deposited By: | Liza Porijo |
Deposited On: | 15 Aug 2011 05:41 |
Last Modified: | 08 Oct 2017 01:13 |
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