Spam E-Mail Characterization: An Experimental Performance Comparison Of Machine Learning

Avijit Mallik, Sabbir Ahmad, Md. Arman Arefin, Sarwar Hosen
2017 Zenodo  
The increasing volume of unsolicited mass e-mail (otherwise called spam) has generated a need for reliable against spam filters. Utilizing a classifier based on machine learning techniques to naturally filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning calculations applied to different parts of e-mail. Experimental
more » ... Experimental results reveal that the header of e-mail provides very useful data for all the machine learning calculations considered to detect spam e-mail.
doi:10.5281/zenodo.1016645 fatcat:c46mmbrjq5eenmz3665htt2ply