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Association Rule Mining for Suspicious Email Detection: A Data Mining Approach

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3 Author(s)
Appavu, S. ; Thiagarajar Coll. of Eng., Tamilnadu ; Muthu Pandian ; Rajaram, R.

Email has been an efficient and popular communication mechanism as the number of Internet user's increase. In many security informatics applications it is important to detect deceptive communication in email. This paper proposes to apply Association Rule Mining for Suspected Email Detection. (Emails about Criminal activities).Deception theory suggests that deceptive writing is characterized by reduced frequency of first person pronouns and exclusive words and elevated frequency of negative emotion words and action verbs . We apply this model of deception to the set of Email dataset, then applied Apriori algorithm to generate the rules The rules generated are used to test the email as deceptive or not. In particular we are interested in detecting emails about criminal activities. After classification we must be able to differentiate the emails giving information about past criminal activities(Informative email) and those acting as alerts(warnings) for the future criminal activities. This differentiation is done using the features considering the tense used in the emails. Experimental results show that simple Associative classifier provides promising detection rates.

Published in:

Intelligence and Security Informatics, 2007 IEEE

Date of Conference:

23-24 May 2007

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