Analysis of Spam Filtering between Fuzzy Logic and Other Machine Learning Technique
K.P. Rajesh, K. K.Summitra · SSRN Electronic Journal · 2010
Unsolicited bulk email (spam) is a major problem on the Internet. To counter spam, several techniques, ranging from spam filters to mail protocol extensions like hash cash, have been proposed. In this paper we investigate the effectiveness of several spam filtering techniques and technologies. The filtering is done by using genetic algorithms and result is compared with the Bayesian classifier. Our analysis was performed by simulating email traffic under different conditions. We show that genetic algorithm based spam filters perform best at server level and naive Bayesian filters are the most appropriate for filtering at user level. The results are promising as compared with a naive Bayesian classifier. Classification accuracy above 97% and low false positive rates are achieved in many test cases.