Experimental analysis on the anti spam effectiveness - commercial, Bayesian and Ngram algortihm / Ahmad Kamal Ramli

With the latest technology and of mail server it exploits the potential of spamming activities to be increase accordingly. People doing spam on they own ways and for their own reasons. Spam is not a virus and it does not contain viruses. Marketing agencies using automated spam structure to perform mass mailing on their promotion and marketing events. At present they are few solutions for tackle spam, using commercial, open source and third party organizations to filter the incoming and outgoing messages. By comparing commercial, Bayesian and N-gram algorithm in rejecting spam and predicting ham messages, it will be a very significant research project for choosing the rights tool to minimize spam activities. By using standard series of text messages which consists of spam and ham words, N-Gram algorithm performed very well. It has the ability to predicting the next alphabet and this is much different with Bayesian algorithm. By applying N-Gram in commercial products, user may receive lots of ham messages inside their inbox. From the test itself Bayesian able to detect only 66.66 % accuracy of ham words inside series of messages. However ,N-Gram score 100% for the same exercise and the algorithm itself have the capability to increase the potential of ham or spam weightage.