An Efficient Spam Filtering using Supervised Machine Learning Techniques

Deepika Mallampati¹

1. Department of Computer Science, SREYAS INSTITUTE OF ENGINEERING & TECHNOLOGY, Jawaharlal Nehru Technological University, Hyderabad, India.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.33-37, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.3337

Online published on Apr 30, 2018

Copyright © Deepika Mallampati . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Email spam or junk e-mail (unsolicited e-mail “usually of a commercial nature sent out in bulk”) is one of the major problem of the today`s Internet, carrying financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters comprise organizing incoming email and removal of spam and computer viruses. In proposed work, we employed supervised machine learning techniques to filter the email spam messages. Extensively used supervised machine learning techniques namely C 4.5 Decision tree classifier, Multilayer Perceptron, Naïve Bayes Classifier are used for learning the features of spam emails and the model is built by training with known spam emails and legitimate emails.

Key-Words / Index Term :
Spam, SpamFilter, Supervised Machine Learning

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