Naive bayes machine learning framework for auto detection of spam mails

Nowadays, Email spam is a major problem, with the increase usage of internet for product promotions, e-banking, e-loans, health related articles, movie news etc to users. Besides the people are practicing unwanted and unethical conduct as in phishing and mailing fraudulent information. Sending suspicious links through spam emails can harm our system and can also be sought after our system. Creating a fake user identity and email account is such easy for spammers. They pretend like a genuine entity in their spam emails and target those people who are not aware of these frauds. So, it is essential to identify those spam emails which are fraud. In this proposed work, we have applied machine learning algorithm to identify whether the incoming mail is spam or ham. This project will discuss how the Naive Bayes machine learning algorithm is applied on our data sets and how it has been selected for the email spam detection with the best precision and accuracy

Computer security · Forum spam · Harm · Identity Theft · Internet privacy · Internet users · Machine learning · Naive Bayes classifier · Phishing · Product (mathematics · Spambot · Spamming · Support vector machine · The Internet · World Wide Web · Computer Science · Network Security and Intrusion Detection · Spam and Phishing Detection · Text and Document Classification Technologies · Artificial Intelligence