Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique

• Published in: Advanced Informatics for Computing Research Vol. 712; pp. 18 - 30
• Main Authors: Choudhary, Neelam, Jain, Ankit Kumar
• Format: Book Chapter
• Language: English
• Published: Singapore Springer 2017; Springer Singapore
• Series: Communications in Computer and Information Science
• Subjects: Feature selection; Machine learning; Mobile devices; SMS spam
• ISBN: 9789811057793; 9811057796
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-981-10-5780-9_2

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.