[Show abstract][Hide abstract] 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.
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