Message Spam Identification by Naive Bayes Classifier Algorithm using Machine Learning

Abstract

With the spread of modern life, messaging has become one of the most important forms of communication. SMS (Short Message Service) is a text messaging service available on all smart phones and mobiles. Facebook, WhatsApp etc. Unlike other chat- based communication applications, SMS does not require any internet connection. SMS traffic has increased significantly and spam has also been increased rapidly. Hackers and spammers are trying to scam over devices through SMSs. As a result, SMS support for mobile devices becomes difficult. Spammers may ask for business expansion, lottery information, credit card information, etc. They also try to send spam emails to obtain financial or commercial benefits such as: attackers attempt to disrupt the system by sending spam links that, when clicked, allow them to control mobile devices. To analyze this communication, the authors developed a system that can analyze malicious messages and determine whether they are RAW or SPAM. Here, we use text classification methods such as Naive Bayes classifier algorithm to classify the texts and determine the message whether it is spam or not. Keywords:- Machine Learning, Language Processing, Spam, Ham, SMS, Naive Bayes, Logistic Regression.