SPAM AND EMAIL DETECTION IN BIG DATA PLATFORM USING NAIVES BAYESIAN CLASSIFIER

Email spam is operations which are sending the undesirable messages to different email client. E-mail spam is the very recent problem for every individual. The e-mail spam is nothing it's an advertisement of any company/product or any kind of virus which is receiving by the email client mailbox without any notification. To solve this problem the different spam filtering technique is used. The spam filtering techniques are used to protect our mailbox for spam mails. In this project, we are using the Naives Bayesian Classifier with three layer framework that includes obfuscator, classifier and anomaly detector for spam classification for bulk emails. The Naïve Bayesian Classifier is very simple and efficient method for spam classification. Here we are using the real time dataset for classification of spam and non-spam mails. The feature extraction technique is used to extract the feature in terms of digest based on bucket classification. The result is to increase the accuracy of the system. And implement Self Acknowledgeable Intranet Mail System has been designed and implemented to benefit the sender about the status of his mail. Once a mail is sent, the sender can know the receiver activity in the mail system until the mail is viewed. Finally provide the pop up window to identify the mail content at the time of open the spam mails.