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Available from: Juan Manuel Corchado Rodríguez, Mar 19, 2015
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    • "In work [13] there is described the anti-spam system ACABARASE developed on the basis of CBR which after certain training filters spam with less false-positive cases. The spam filtration model SPAMHUNTING presented in works [14] [15] [16] [17] [18] also based on CBR, which applies the disjoint knowledge representation engine. This spam filter able to address the concept drift problem by combining a relevant term identification technique with an evolving sliding window strategy. "
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    ABSTRACT: Earlier works on detecting spam e-mails usually compare the contents of e-mails against specific keywords, which are not robust as the spammers frequently change the terms used in e-mails. We have presented in this paper a novel featuring method for spam filtering. Instead of classifying e-mails according to keywords, this study analyzes the spamming behaviors and extracts the representative ones as features for describing the characteristics of e-mails. An back-propagation neural network is designed and implemented, which builds classification model by considering the behavior-based features revealed from e-mails’ headers and syslogs. Since spamming behaviors are infrequently changed, compared with the change frequency of keywords used in spams, behavior-based features are more robust with respect to the change of time; so that the behavior-based filtering mechanism outperform keyword-based filtering. The experimental results indicate that our methods are more useful in distinguishing spam e-mails than that of keyword-based comparison.
    Applied Intelligence 01/2009; 31:107-121. DOI:10.1007/s10489-008-0116-0 · 1.85 Impact Factor