SMS Spam Detection Framework Using Machine Learning Algorithms and Neural Networks

Abstract

In our current generation we are very much habituated to many mobile services like communication, ecommerce etc. In mobile communication services SMS’s (Short Message Service’s) are very common and important services which we are using in personal purposes and profession. In these services some messages may cause spam attacks which is trap to users to access their personal information or attracting them to purchase a product from unauthorized websites. It is very easy for companies send any information or service or alert to their customers/users with these SMS API’s. Based on these services it is also possible for sending spam messages. So in this system we are using advance Machine Learning concepts for detection of the spam filtering in the SMS’s. In this system we are importing the dataset from UCI repository and for spam SMS detection we implementing machine learning classifiers like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Networks (NN) algorithms and with their metrics like accuracy, precision, recall and f-score. We calculate performances between there algorithms as well as we show the experiment results with visualization techniques and analyses which algorithm is best for spam SMS detection.

Spam SMS Classifier using Machine Learning Algorithms

“…The identification of SMS spam is a relatively new topic of study, following the detection of spam in text messages, emails with social media attachments, tweets, and websites. Several studies on spam detection include [1,2] and others. These studies are typically carried out after in the last few years.…”

“…There are many reasons why the amount of spam messages is rising. First of all, a large portion of the global population uses mobile devices, making a large portion of that population susceptible to spam communications [1,2,6]. Second, the spammer may benefit from the low cost of sending spam messages [2,4].…”