Classification Methods in the Detection of New Suspicious Emails

S. Appavu Alias Balamurugan (), G. Athiappan, M. Muthu Pandian and R. Rajaram
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S. Appavu Alias Balamurugan: Department of Information Technology, Thiagarajar College of Engineering, Madurai-625015, India
G. Athiappan: Department of Information Technology, Thiagarajar College of Engineering, Madurai-625015, India
M. Muthu Pandian: Department of Information Technology, Thiagarajar College of Engineering, Madurai-625015, India
R. Rajaram: Department of Computer Science & Engineering, Thiagarajar College of Engineering, Madurai-625015, India

Journal of Information & Knowledge Management (JIKM), 2008, vol. 07, issue 03, 209-217

Abstract: Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

Keywords: Data mining; deceptive theory; decision tree; neural network; Naïve Bayes; SVM; WEKA (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1142/S0219649208002044

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