Bayesian filter works efficiently by comparing email content (phrases or tokens) against stored database. This paper presents a discussion about the implementation of binomial distribution and Poisson distribution in Bayesian spam filter. This approach is beneficial for calculating the probability of a mail being spam, containing words that are not stored in database (i.e., encountered by the filter for the first time) or rare words (less frequent words) and for reducing and controlling false positive.
['Redwan Zakariah', 'Samina Ehsan']
Detecting junk mails by implementing statistical theory