In this paper, we proposed an efficient spamfiltering method based on decision tree data mining technique, analyzed the as-sociation rules about spams, and applied these rules to develop a systematized spamfiltering method. Our method possessedthe following three major superiorities: (i) checking only an e-mail’s header section to avoid the low-operating efficiency inscanning an e-mail’s content. Moreover, the accuracy offiltering was enhanced simultaneously. (ii) In order that the probablemisjudgment in identifying an unknown e-mail could be“reversed”, we had constructed a reversing mechanism to help theclassification of unknown e-mails. Thus, the overall accuracy of ourfiltering method will be increased. (iii) Our method wasequipped with a re-learning mechanism, which utilized the supervised machine learning method to collect and analyze eachmisjudged e-mail. Therefore, the revision information learned from the analysis of misjudged e-mails incrementally gavefeedback to our method, and its ability of identifying spams would be improved.
Security and Communication Networks 【SCI-E】, Vol.9, No.17, pp.4013-4026