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SMS Spam Detection Framework Using Machine Learning Algorithms and Neural Networks

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.10, No. 6)

Publication Date:

Authors : ; ; ; ; ;

Page : 10-19

Keywords : Short Message Service; Support Vector Machine; K-Nearest Neighbor; Naïve Bayees;

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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.

Last modified: 2021-06-10 16:31:45