Spam email detection using machine learning algorithm

Abstract

Nowadays the communication between the organizations or any individuals became easier by use of Electronic mail method. The internet users are increasing rapidly day by day and also spams are increasing with the emails. It was an easy task for the spammers to create an email account and making a fake profile. Therefore, detecting of these spam mails that were fraud is of most important. This paper aims to develop a proposed approach of data science for spam email detection (SMD) using machine learning algorithm. A hybrid bagging approach is used in this proposed method for the detection of spam emails which implements the two, Naïve Bayes and J48 (i.e. decision tree) machine learning algorithms. Each of these algorithms is applied as an input with a data set which is partitioned in to different sets using the data science. Emails classification can be accomplished depends on the patterns of repetitive keywords and several additional parameters like Cc (carbon copy) or Bcc (Blind carbon copy), domain, header etc. that are present in their structure. Whenever the machine learning algorithm is applied for the email classification every one of those parameters are considered as a features. This proposed approach of data science for spam mail detection using machine learning algorithm achieved a 88.12% of overall accuracy with the hybrid bagged approach implementation.