Schematizing a Global SPAM Indicative Probability

In this paper we propose a middleware infrastructure to address the problem of filtering unsolicitated mail messages (known as SPAM). In our approach we use Bayesian Classifications of SPAM messages built upon categorization models that map a probability to a word using text analysis not only to unsolicitated mails but also to legitimate mail messages, making easier to extract a cumulate inference about the nature of the e-mail message. Our proposed architecture is based on the extension of these models using the advances of Collaborative Filtering Methods expressed via Peer-to-peer networks will help to built more effective and accurate anti-spam filters