Abstract:

The popularity of mobile devices is increasing day by day as they provide a large variety of services by reducing the cost of services. Short Message Service (SMS) is considered one of the widely used communication service. However, this has led to an increase in mobile devices attacks like SMS Spam. In this paper, we present a novel approach that can detect and filter the
spam messages using machine learning classification algorithms. We study the characteristics of spam messages in depth and then found ten features, which can efficiently filter SMS spam messages from ham messages. Our proposed approach achieved 96.5% true positive rate and 1.02% false positive rate for Random Forest classification algorithm.

Keywords:
SMS spam ,
Mobile devices ,
Machine learning _ Feature selection

Objective:

The main aim of the project it can detect and filter the spam messages using machine learning classification algorithms. We have used a feature set of features for classification. These features can differentiate a spam SMS from ham SMS. Machine learning techniques were effective in email spam filtering as it helps in preventing zero-day attacks and provides the high level of security.