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- W2056579427 abstract "Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. More than 85% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques like sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many current solutions feature machine-learning algorithms trained using statistical representations of the terms that most commonly appear in such e-mails. However, there are attacks that can subvert the filtering capabilities of these methods. Tokenization attacks, in particular, insert characters that create divisions within words, causing incorrect representations of e-mails. In this paper, we introduce a new method that reverses the effects of tokenization attacks. Our method processes e-mails iteratively by considering possible words, starting from the first token and compares the word candidates with a common dictionary to which spam words have been previously added. We provide an empirical study of how tokenization attacks affect the filtering capability of a Bayesian classifier and we show that our method can reverse the effects of tokenization attacks." @default.
- W2056579427 created "2016-06-24" @default.
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- W2056579427 date "2013-01-01" @default.
- W2056579427 modified "2023-10-16" @default.
- W2056579427 title "JURD: Joiner of Un-Readable Documents to reverse tokenization attacks to content-based spam filters" @default.
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- W2056579427 doi "https://doi.org/10.1109/ccnc.2013.6488455" @default.
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