Currently the spam filtering technology is based on the naive Bayesian model. Because of the extremely complex semantic environment as well as naive Bayesian algorithms are easily deceiving; it is not very good at spam filtering. The recent method presented is SOcial network Aided Personalized and effective spam filter (SOAP) using Bayesian spam filtering technique. SOAP showing better results as compared to existing methods, but still it can be further improved in terms of accuracy, efficiency and complexity. In this paper we are presenting extension to SOAP method termed as ISOAP (Improved SOAP) by using RBF (Radial Basis Function) neural network rather than naïve Bayes method for spam filtering. Unlike previous spam filters that focus on parsing keywords or building blacklists, ISOAP exploits the social relationships among email correspondents and their (dis) interests to detect spam adaptively and automatically.

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Spam Filtering Bayesian Spam Filters Social Networks Rbf Spam Filter.