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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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IEEE Style Citation: Deepika Mallampati, “An Efficient Spam Filtering using Supervised Machine Learning Techniques,” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.33-37, 2018.

MLA Style Citation: Deepika Mallampati "An Efficient Spam Filtering using Supervised Machine Learning Techniques." International Journal of Scientific Research in Computer Science and Engineering 6.2 (2018): 33-37.

APA Style Citation: Deepika Mallampati, (2018). An Efficient Spam Filtering using Supervised Machine Learning Techniques. International Journal of Scientific Research in Computer Science and Engineering, 6(2), 33-37.

BibTex Style Citation:
@article{Mallampati_2018,
author = {Deepika Mallampati},
title = {An Efficient Spam Filtering using Supervised Machine Learning Techniques},
journal = {International Journal of Scientific Research in Computer Science and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {2},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {33-37},
url = {https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=604},
doi = {https://doi.org/10.26438/ijcse/v6i2.3337}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i2.3337}
UR - https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=604
TI - An Efficient Spam Filtering using Supervised Machine Learning Techniques
T2 - International Journal of Scientific Research in Computer Science and Engineering
AU - Deepika Mallampati
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 33-37
IS - 2
VL - 6
SN - 2347-2693
ER -

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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

References :
[1] Aladdin Knowledge Systems, “Anti-spam white paper,
www.csisoft.com/security/aladdin/esafe_antispam”, Retrieved December 28, 2011.
[2] F. Smadja, H. Tumblin, "Automatic spam detection as a text classification task", in: Proc. of Workshop on Operational Text Classification Systems, 2002.

[3] Ann Nosseir , Khaled Nagati and Islam Taj-Eddin, “Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 1, March 2013 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784.

[4] R. Kishore Kumar, G. Poonkuzhali, P. Sudhakar,” Comparative Study on Email Spam Classifier using Data Mining Techniques”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2012 Vol I, IMEC2012, March 14-16,2012, Hong Kong, ISBN: 977-988-19251-1-4.

[5] Rafiqul Islam and Yang Xiang, member IEEE, “Email Classification Using Data Reduction Method” created June 16, 2010.

[6] Asmeeta Mali, “Spam Detection Using Baysian with Pattren Discovery”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-3, July 2013.

[7] Vandana Jaswal, “ Spam Detection System Using Hidden Markov Model”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013 ISSN: 2277 128X.
[8] Saadat Nazirova, “Survey on Spam Filtering Techniques”, Communications and Network, 2011, 3, 153 160, doi:10.42 36/cn.2011.33019 Published Online August 2011 (http: //www.SciRP.org /journal/cn).

[9] Neha Singh,”Dendritic Cell Algorithm and Dempster Belief Theory Using Improved Intrusion Detection System “, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013 ISSN: 2277 128X.

[10] Julie Greensmith, “The Dendritic Cell Algorithm”, Thesis submitted to the University of Nottingham for the degree of Doctor of Philosophy October 2007.

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