A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques

Abstract

In this age of popular instant messaging applications, Short Message Service or SMS has lost relevance and has turned into the forte of service providers, business houses, and different organizations that use this service to target common users for marketing and spamming. A recent trend in spam messaging is the use of content in regional language typed in English, which makes the detection and filtering of such messages more challenging. In this work, an extended version of a standard SMS corpus containing spam and non-spam messages that is extended by the inclusion of labeled text messages in regional languages like Hindi or Bengali typed in English has been used, as gathered from local mobile users. Monte Carlo approach is utilized for learning and classification in a supervised approach, using a set of features and machine learning algorithms commonly used by researchers. The results illustrate how different algorithms perform in addressing the given challenge effectively.