A Competent Spam Prediction Technique by Supervised Deep Learning Classifiers
Durga Maariappan K, D. Subba Rao, B Giridhar, T. Sangeetha · SSRN Electronic Journal · 2020
Since the last couple of decades, mobile phone users have been rapidly increased as the SMS are easy, less expensive and independent upon cell phone operating system. SMS has become one of the popular communication media throughout the world. 3.5 billion or 80% active users, throughout the word, use mobile SMS as a communication medium. Out of this huge number of SMS, a large number of SMS are spam, generated by offenders for a number of reasons. Due to the spam SMS, criminal gangs become stronger and perform different criminal activities. Short Message Service (SMS) has become one of the most important media of communications due to the rapid increase of mobile users and it’s easy to use operating mechanism. This flood of SMS goes with the problem of spam SMS that are generated by spurious users. The detection of spam SMS has gotten more attention of researchers in recent times and is treated with a number of different machine learning approaches. The traditional methods do not produce good results. In this work, Proposed approach uses deep learning classifiers to predict spam messages.