We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

A New Probability based Analysis for Recognition of Unwanted Emails

by Shashi Kant Rathore, Palvi Jassi, Basant Agarwal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 4
Year of Publication: 2011
Authors: Shashi Kant Rathore, Palvi Jassi, Basant Agarwal
10.5120/3378-4673

Shashi Kant Rathore, Palvi Jassi, Basant Agarwal . A New Probability based Analysis for Recognition of Unwanted Emails. International Journal of Computer Applications. 28, 4 ( August 2011), 6-9. DOI=10.5120/3378-4673

@article{ 10.5120/3378-4673,
author = { Shashi Kant Rathore, Palvi Jassi, Basant Agarwal },
title = { A New Probability based Analysis for Recognition of Unwanted Emails },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 4 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number4/3378-4673/ },
doi = { 10.5120/3378-4673 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:13:50.966803+05:30
%A Shashi Kant Rathore
%A Palvi Jassi
%A Basant Agarwal
%T A New Probability based Analysis for Recognition of Unwanted Emails
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 4
%P 6-9
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electronic mail is used as a mean for personal and business communication. The volume of unwanted messages or mails that are received is growing as well. Cost of sending this type of Email is very low for sender, so several people and companies use it to quickly distribute unsolicited bulk messages, also called spam, to a large number of recipients. The reasons for sending spam vary and may include marketing of products and services. Moreover, many people uses spam as a medium for attacks and distributing harmful content such as viruses, trojan horses, worms and other malware. Spam has become a major threat for business users, network administrators and even ordinary users. In addition to regulations, several technical solutions including commercial and open source products have been proposed and deployed to block this problem. In this work proposed and implement mechanism for block spam mails by implementing anti spam filters at the network gateway.

References
  1. D. Heckerman, “Anti SPAM System: Another Way of Preventing SPAM”, Proceeding of the 16th international on database and Expert Systems Applications 1529-4188/05 on 5-9 Sept. Page(s):1 – 5, 2005.
  2. Tarek Hassan, Peter Cole, “An Intelligent Spam Filter”, School of information Technology, 2008. 28-30 Page(s):466 – 473, Aug. 2008.
  3. J. Provost, “Naïve-Bayes vs. rule-learning in classification of email,” The University of Texas at Austin, Department of Computer Sciences, Technical Report AI-TR-99-284, : 4-13, 2003.
  4. E.-S. M. El-Alfy and R. E. Abdel-Aal, “Spam filtering with abductive networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN’08), Hong Kong, :1-3, June 2008.
  5. E.-S. M. El-Alfy, “Learning Methods for Spam Filtering,” International Journal of Computer Research, vol. 16, no. 4, 2008.
  6. Y. Yang, S. Elfayoumy, “Anti-spam filtering using neural networks and Bayesian classifiers,” in Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Jacksonville, FL, USA, : 13-22, June 2007.
  7. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz, “A Bayesian approach to filtering junk e-mail,” in Proceedings of AAAI’98 Workshop on Learning for Text Categorization, Madison, WI, :8-12, July 1999.
  8. Cournane, A., & Hunt, R. “An Analysis of the tools used for the generation and prevention of spam.” Computer & Security, 23 (2), 154- 166, 2004.
  9. Bayesian Technique. Retrieved 10th September 2004. (Accessed from http://classifier4j.sourceforge.net ).
  10. T. A. Meyer and B. Whateley. Spambayes: “Effective open-source, bayesian based, email classification system”. In Proceedings of the First Conference on Email and Anti-Spam (CEAS), 2004.
  11. Spertus, E. Smokey: “Automatic Recognition of Hostile Messages”. Proceedings of the 14th National Conference on AI and the 9th Conference on Innovative Applications of AI, pp. 1058–1065, Providence, Rhode Island, 2000.
  12. Payne, T.R. and Edwards, P. Interface Agents that Learn: “An Investigation of Learning Issues in a Mail Agent Interface”. Applied Artificial Intelligence, 11(1):1–32, 1999.
  13. Hall, R.J. “How to Avoid Unwanted Email”. Communications of ACM, 41(3):88–95, 2004.
  14. Patrick Pantel and Dekang Lin. Spamcop: “A spam classification and organization program”. In Learning for Text Categorization: Papers from the 2006 Workshop, Madison, Wisconsin, AAAI Technical Report, 2006.
  15. John Aycock & Nathan Friess, “Spam Zombies from Outer Space” Department of Computer Science University of Calgary, 15th Annual EICAR Conference, pages: 23-31, 2001.
  16. Cohen,W. “Learning rules that classify e-mail”. In Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access. Palo Alto, California, 18–25, 2003.
  17. Quinlan, J.R. “C4.5: Programs for Machine Learning”. Morgan Kaufmann, pages: 44-59, 2002.
  18. G. Sakkis, I. Androutsopoulos, and G. Paliouras, “A memory-based approach to anti-spam filtering,” Information Retrieval, vol. 6, pp. 49- 73,8-3-2003.3.
Index Terms

Computer Science
Information Sciences

Keywords

Spam filtering text categorization machine learning legitimate emails unsolicited commercial e-mail spam