CFP last date
02 December 2024
Call for Paper
January Edition
CAE solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 02 December 2024

Submit your paper
Know more
Reseach Article

Enhancing the Rate of Accuracy and Precision in Spam Filtering in Farsi SMS

by Maryam Poorshahsavari, Omid Pourgalehdari
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 3
Year of Publication: 2015
Authors: Maryam Poorshahsavari, Omid Pourgalehdari
10.5120/cae2015651906

Maryam Poorshahsavari, Omid Pourgalehdari . Enhancing the Rate of Accuracy and Precision in Spam Filtering in Farsi SMS. Communications on Applied Electronics. 3, 3 ( October 2015), 25-27. DOI=10.5120/cae2015651906

@article{ 10.5120/cae2015651906,
author = { Maryam Poorshahsavari, Omid Pourgalehdari },
title = { Enhancing the Rate of Accuracy and Precision in Spam Filtering in Farsi SMS },
journal = { Communications on Applied Electronics },
issue_date = { October 2015 },
volume = { 3 },
number = { 3 },
month = { October },
year = { 2015 },
issn = { 2394-4714 },
pages = { 25-27 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume3/number3/448-2015651906/ },
doi = { 10.5120/cae2015651906 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:43:56.598864+05:30
%A Maryam Poorshahsavari
%A Omid Pourgalehdari
%T Enhancing the Rate of Accuracy and Precision in Spam Filtering in Farsi SMS
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 3
%P 25-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduces a technique for increasing the rate of accuracy in spam filtering and reducing the false positive (fp) in Farsi SMS. This technique is based on combination of naïve bayes assumption with an introduced formula to increase the filtering accuracy up to 90%. In order to validate introduced formula and to measure the accuracy, the obtained results have been surveyed by precision-Recall techniques.

References
  1. Almeida, T. A., Hidalgo, J. M. G., & Yamakami, A. (2011, September). Contributions to the study of SMS spam filtering: new collection and results. In Proceedings of the 11th ACM symposium on Document engineering (pp. 259-262). ACM.
  2. Delany, S. J., Buckley, M., & Greene, D. (2012). SMS spam filtering: methods and data. Expert Systems with Applications, 39(10), 9899-9908.
  3. Nuruzzaman, M. T., Lee, C., & Choi, D. (2011, August). Independent and personal SMS spam filtering. In Computer and Information Technology (CIT), 2011 IEEE 11th International Conference on (pp. 429-435).
  4. Alzahrani, A. J., & Ghorbani, A. A. (2014, May). SMS mobile botnet detection using a multi-agent system: research in progress. In Proceedings of the 1st International Workshop on Agents and Cyber Security (p. 2). ACM.
  5. http://blog.melipayamak.com/posts/look-at-the-history-of-sms-in-the-world/
  6. Mahmoud, T. M., & Mahfouz, A. M. (2012). SMS spam filtering technique based on artificial immune system. IJCSI International Journal of Computer Science Issues, 9(1).
  7. Sethi G, Bhootna V ( 2014) “SMS Spam Filtering Application Using Android “International Journal of Computer Science and Information Technologies, Vol. 5 (3) ,(pp 4624-4626) IJCSIT.
  8. Narayan, A., & Saxena, P. (2013, November). The curse of 140 characters: evaluating the efficacy of SMS spam detection on android. In Proceedings of the Third ACM workshop on Security and privacy in smartphones & mobile devices (pp. 33-42). ACM.
  9. Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
Index Terms

Computer Science
Information Sciences

Keywords

SMS Filtering Naïve bayes Assumption Spam SMS Data Mining False Positive