September06

Innovation with Bayesian Filtering

 

Bayesian filtering is based on the principle that any event is dependent and that the probability of an event occurring in the future can be inferred from the history of occurrences of that event: history is always repeating (Various scientific researches have been made on Bayesian behavior http://www-ccrma.stanford.edu/~jos/bayes/Bayesian_Parameter_Estimation.htm).

Bayesian spam filtering has become a popular mechanism to distinguish illegitimate spam email from legitimate email (sometimes called "ham"). Before email can be filtered using this method, the administrator needs to generate a database with words, tokens, phrases, IP addresses, domains and so on collected from a sample of spam mail and valid mail (usually referred to as ‘ham’). This has to be set up in the very beginning of the process. Then, a probability value is assigned to each word or token; the probability is based on calculations that take into account how often that word occurs in spam as opposed to legitimate mail (ham).

This probability is influenced by the initial set-up of the database: analyzing the users' outbound mail and by analyzing known spam: all the tokens in both pools of email are analyzed to generate the probability that a particular word points to the email being spam. Calculating the SPAM probabilities takes into account, as an essential factor, the number of incoming emails on a certain period of time. It is, hence, essential to make sure that you receive a big number of emails (i.e – 200 in 1 week) in order to build a comprehensive SPAM database. Visendo Innovation: mixing a number of already existing algorithms, your Visendo spam filter can build a reliable SPAM database faster than any other competitive product on the market without having to receive a big number of emails at a certain frequency.

So, if you're running a small - medium company and you are not going to receive 300 emails in the next week, then you should try Visendo Mail Checker Server. For further details about Bayesian filtering, read the following Whitepaper.

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