CONTENT BASED SPAM FILTERING IN EMAIL USING NAIVE BAYES CLASSIFIER

CONTENT BASED SPAM FILTERING IN EMAIL USING NAIVE BAYES CLASSIFIER
Author(s):
Raveena. K, K. Chandra Prabha
Keywords:
spam filters, naive spam, content based spam
Abstract
Email spam keeps on turning into an issue on the Internet. Spammed email may contain numerous duplicates of a similar message, business commercial or other insignificant posts like obscene substance. In past research, distinctive filtering methods are utilized to identify these messages, for example, utilizing Random Forest, Naive Bayesian, Support Vector Machine (SVM) and Neutral Network. In this examination, we test Naive Bayes calculation for email spam filtering on two informational collections and test its execution, i.e., Spam Data and SPAM BASE informational indexes. The execution of the informational indexes is assessed in view of their exactness, review, accuracy and F-measure. Our exploration utilize WEKA instrument for the assessment of Naive Bayes calculation for email spam filtering on the two informational collections. The outcome demonstrates that the kind of email and the quantity of examples of the informational index has an impact towards the execution of Naive Bayes.
Article Details
Unique Paper ID: 146306 Publication Volume & Issue: Volume 4, Issue 12 Page(s): 127 - 130
Article Preview & Download

Share This Article

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org