Suspicious E-mail Detection via Decision Tree: A Data Mining Approach

Appavu Balamurugan, Ramasamy Rajaram


This paper proposes a new method to detect unusual and deceptive communication in email data. 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 ID3 algorithm to generate the decision tree .The decision tree that is generated is used to test the email as deceptive or not. In particular we are interested in detecting fraudulent and possibly criminal behavior from such data.

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