An Efficient Three-phase Email Spam Filtering Technique
Tarek M Mahmoud, Alaa Ismail El Nashar, Tarek Abd-El-Hafeez, Marwa Khairy
Journal of Advances in Mathematics and Computer Science · pp. 1184–1201 · Published 25 Feb 2014
10.9734/BJMCS/2014/7675Abstract
Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Many spam filtering techniques based on supervised machine learning algorithms have been proposed to automatically classify messages as spam or legitimate (ham). Naive Bayes spam filtering is a popular mechanism used to distinguish spam email from ham email. In this paper, we propose an efficient three-phase email spam filtering technique: Naive Bayes, Clonal Selection and Negative Selection. The experimental results applied on 10,000 email messages taken from the TREC 2007 corpus shows that when we apply the Clonal selection and Negative selection algorithms with the naive Bayes spam filtering technique the accuracy rate is increased than applying each technique alone.
Cited by 15
Guang-Li Wu, Xingyue Wang, Qian Lu · Expert systems with applications · 2024
E. Eliwa, Amr Mohamed El Koshiry, Tarek Abd El-Hafeez · Scientific Reports · 2023
Ye Shao, Quan Shi, Yanghua Xiao · 2017
D. Sen, Chandan Das, Sarit Chakraborty · 2017
Nikhil Mathew, V. Bai · International Conference on Emerging Technological Trends · 2016
Tarek M. Mahmoud, Tarek Abd-El-Hafeez, D. El-Deen · 2016
S. Khan, P. Nair · 2016
Archana Sahu, A. Mishra, S. K. Sahu · 2016
Ahmed Omar, Tarek Abd El-Hafeez · Neural Computing and Applications · 2023
Showing 13 of 15 known citations — external sources report more than can currently be individually listed.
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