E-mail Spam Filtering Using Adaptive Genetic Algorithm

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Author(s)

Jitendra Nath Shrivastava 1,* Maringanti Hima Bindu 2

1. Singhania University, Jhunjhunu, Pacheri Bari, Rajasthan, India

2. Deptt.of Computer Science and Applications, North Orissa University, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2014.02.07

Received: 11 May 2013 / Revised: 10 Sep. 2013 / Accepted: 5 Nov. 2013 / Published: 8 Jan. 2014

Index Terms

Spam Filtering, Genetic Algorithm, SPAM and HAM

Abstract

Now a day’s everybody email inbox is full with spam mails. The problem with spam mails is that they are not malicious in nature so generally don’t get blocked with firewall or filters etc., however, they are unwanted mails received by any internet users. In 2012, more that 50% emails of the total emails were spam emails. In this paper, a genetic algorithm based method for spam email filtering is discussed with its advantages and dis-advantages. The results presented in the paper are promising and suggested that GA can be a good option in conjunction with other e-mail filtering techniques can provide more robust solution.

Cite This Paper

Jitendra Nath Shrivastava, Maringanti Hima Bindu, "E-mail Spam Filtering Using Adaptive Genetic Algorithm", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.2, pp.54-60, 2014. DOI:10.5815/ijisa.2014.02.07

Reference

[1]Enrico Blanzieri and Anton Bryl, "A Survey of Learning-Based Techniques of Email Spam Filtering" Conference on Email and Anti-Spam, 2008.

[2]http: //www.kaspersky.com /about/news/spa m/2013/Spam _in_2012_ Continue d_Decline_Sees _Spam _Levels _Hit_5_year_Low.

[3]Youn, Seongwook, and Dennis McLeod, 2007, A Comparative Study for Email Classification, Advances and Innovations in Systems, Computing Sciences and Software Engineering, pp. 387-391

[4]Liu Pei-yu, Zhang Li-wei and Zhu Zhen-fang, "Research on Email Filtering Based on Improved Bayesian", Journal of Computers, v4, 2009, pp. 271-275.

[5]C. Cortes and V. Vapnik, "Support-vector networks. Machine learning", v20, 1995, pp. 273–297.

[6]N.Cristiatnini and J. Shawe-Taylor, "An introduction to Support Vector Machines and Other Kernel-Based Learning Methods," Cambridge University Press, 2003. http://www.support-vector.net.

[7]C. J. C. Burges, "A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery," v2, 1998, pp.121–167.

[8]H. T. Siegelmann and E. D. Sontag, "On the computational power of neural nets. Journal of Computer and System Sciences," v50, 1995, pp.132–150.

[9]W. S. McCulloch and W. H. Pitts, "A logical calculus of the ideas immanent in nervous activity"

[10]F. Rosenblatt, "Principles of Neurodynamics," Spartan Books, Washington, 1958.

[11]M. Basavaraju and Dr. R. Prabhakar, 2010, A Novel Method of Spam Mail Detection using Text Based Clustering Approach, IJCA, pp. 15-25.

[12]K.S. Tang et. al., "Genetic Algorithm and Their Applications" IEEE Signal Processing magazine, 1996, pp.22-37. 

[13]Rich Drewes "An artificial neural network spam classifier", Project home page: www.interstice.com/drewes/cs676/spam-nn. 

[14]L. Zhang, J. Zhu, and T. Yao, "An evaluation of statistical spam filtering techniques" ACM Transactions on Asian Language Information Processing (TALIP), v3, 2004, pp.243–269.

[15]P. Ferragina and R. Grossi, "Improved dynamic text indexing," J. Algorithms, v31, 1999, pp. 291–319.

[16]J. K¨arkk¨ainen and E. Ukkonen. Lempel-zivparsing and sublinear, "Size index structures for string matching" in Proc WSP’96, Carleton University Press, 1996, pp. 141-155.

[17]Goldberg, D. E., and Deb, K. 1991. A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms. Morgan Kaufmann.

[18]Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection.MIT Press.

[19]Koza, J. R. 1994. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press.