A Comparative Study of Spam SMS Detection Using Machine Learning Classifiers

With technological advancements and increment in content based advertisement, the use of Short Message Service (SMS) on phones has increased to such a significant level that devices are sometimes flooded with a number of spam SMS. These spam messages can lead to loss of private data as well. There are many content-based machine learning techniques which have proven to be effective in filtering spam emails. Modern day researchers have used some stylistic features of text messages to classify them to be ham or spam. SMS spam detection can be greatly influenced by the presence of known words, phrases, abbreviations and idioms. This paper aims to compare different classifying techniques on different datasets collected from previous research works, and evaluate them on the basis of their accuracies, precision, recall and CAP Curve. The comparison has been performed between traditional machine learning techniques and deep learning methods.