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Spam Classification Using Deep Learning Technique

A.B.Singh 1 , S.B.Singh 2 , Kh.M.Singh 3

  1. Dept. of Computer Science and Engineering, National Institute of Technology, Manipur, India.
  2. Dept. of Computer Science and Engineering, National Institute of Technology, Manipur, India.
  3. Dept. of Computer Science and Engineering, National Institute of Technology, Manipur, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 383-386, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.383386

Online published on May 31, 2018

Copyright © A.B.Singh, S.B.Singh, Kh.M.Singh . 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: A.B.Singh, S.B.Singh, Kh.M.Singh, “Spam Classification Using Deep Learning Technique,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.383-386, 2018.

MLA Style Citation: A.B.Singh, S.B.Singh, Kh.M.Singh "Spam Classification Using Deep Learning Technique." International Journal of Computer Sciences and Engineering 6.5 (2018): 383-386.

APA Style Citation: A.B.Singh, S.B.Singh, Kh.M.Singh, (2018). Spam Classification Using Deep Learning Technique. International Journal of Computer Sciences and Engineering, 6(5), 383-386.

BibTex Style Citation:
@article{_2018,
author = {A.B.Singh, S.B.Singh, Kh.M.Singh},
title = {Spam Classification Using Deep Learning Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {383-386},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1990},
doi = {https://doi.org/10.26438/ijcse/v6i5.383386}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.383386}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1990
TI - Spam Classification Using Deep Learning Technique
T2 - International Journal of Computer Sciences and Engineering
AU - A.B.Singh, S.B.Singh, Kh.M.Singh
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 383-386
IS - 5
VL - 6
SN - 2347-2693
ER -

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Abstract

Deep Learning technique which is a new area of Machine Learning is showing huge promise in achieving the original goals of Machine learning: Artificial Intelligence. Deep Learning is being applied in every machine learning problem and has shown great results. In this paper, we evaluate the problem of spam classification using Deep Learning Technique and compare the result with other state-of-art machine learning techniques. The machine learning techniques used in the comparison are: Random Forest, Multinomial Naïve Bayesian and Support Vector Machine. The dataset used in the experiment is the CSDMC_2010 and Enron dataset and the platform used is the WEKA interface. Common features are extracted from the body of the spam and feature vector table is constructed, which is used on all the model. Our experiment shows that Deep Learning model outperform all the other machine learning techniques in terms of true positive & true negative and even in the overall accuracy.

Key-Words / Index Term

Spam, Deep Learning, Machine Learning, Classify, WEKA

References

[1] Thiago S. Guzella and Walmir M. Caminhas, “A review of machine learning approaches to spam filtering”, Expert System with Applications”, Elsevier, Vol-36, pp 10206-10222, 2009.
[2] G. Cormack, “Email spam filtering: A systematic Review", Foundations and Trends in Information Retrieval , Vol-1, no. 4, pp. 335–455, 2008.
[3] M. Sahami, S. Dumais, D. Heckerman and E. Horvitz, “A Bayesian Approach to Filtering Junk Email,” AAAI Technical Report WS-98-05, AAAI Workshop on Learning for Text Categorization, 1998.
[4] Drucker H, Wu D, Vapnik VN. “Support Vector Machines for Spam Categorization”, IEEE Transactions on Neural Networks Vol-10, Issue-5, pp 1048-1054, 1999.
[5] Yudong Zhang, Shuihua Wang, Preetha Phillips, Genlin Ji, “Binary PSO with mutation operator for feature selection using decision tree applied to spam detection”, knowledge-Based Systems, Elsevier, Vol-64, pp 22-31, 2014.
[6] Zhang L, Zhu J, Yao T, “An Evaluation of Statistical Spam Filtering Techniques Spam Filtering as Text Categorization”, ACM Transactions on Asian Language Information Processing (TALIP), Vol-3, Issue 4, pp 243-269, 2004.
[7] Almeida TA, Yamakami A, “Content-Based Spam Filtering”, The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, pp 1-7, 2010.
[8] Lin Li and Chi Li, ”Research and Improvement of a Spam Filter based on Naïve Bayes”, Proceedings of the 2015 Seventh International Conference on Intelligent Human-Machine Systems and Cybernetics, 2015
[9] Amayri O, Bouguila N, “A study of Spam Filtering using Support Vector Machines”, Artificial Intelligence Review, Vol-34, Isuue 1, pp 73-108, 2010.
[10] Koprinska I, Poon J, Clark J, Chan J, “Learning to Classify e-mail”, Information Sciences, Vol-177, issue 10, pp 2167-2187, 2007.