Implementation and Experimental Results of Neural Network and Genetic Algorithm based Spam Filtering Technique 


Vol. 13,  No. 2, pp. 259-266, Apr.  2006
10.3745/KIPSTC.2006.13.2.259


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  Abstract

As the volume of spam has increased to extreme levels, many anti-spam filtering techniques have been proposed. Among these techniques, the machine-learning filtering technique is one of the most popular filtering techniques. In this paper, we propose a machine-learning spam filtering technique based on the neural network, the genetic algorithm and the χ2-statistic. This proposed filtering technique is designed to overcome the problems in existing filtering techniques, and to achieve high spam filtering accuracy. It is able to classify spam and legitimate email with 95.25 percent and 95.31 percent accuracy. This accuracy of the spam filtering is 7.75 percent and the 12.44 percent higher than rule-based filtering and the Bayesian filtering technique, respectively.

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[IEEE Style]

B. B. Kim and H. K. Choi, "Implementation and Experimental Results of Neural Network and Genetic Algorithm based Spam Filtering Technique," The KIPS Transactions:PartC, vol. 13, no. 2, pp. 259-266, 2006. DOI: 10.3745/KIPSTC.2006.13.2.259.

[ACM Style]

Bum Bae Kim and Hyoung Kee Choi. 2006. Implementation and Experimental Results of Neural Network and Genetic Algorithm based Spam Filtering Technique. The KIPS Transactions:PartC, 13, 2, (2006), 259-266. DOI: 10.3745/KIPSTC.2006.13.2.259.

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