EVALUATION OF MACHINE LEARNING ALGORITHMS FOR FILTERING AND ISOLATING SPAMMED MESSAGES
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Date
2020-04-13
Authors
Azeez, Nureni Ayofe
Adio, Esther
Yekinni, Adewale
Onyema, Juliet
Journal Title
Journal ISSN
Volume Title
Publisher
FUTA JOURNAL OF RESEARCH IN SCIENCE
Abstract
The use of mobile application is rising on daily basis as they offer a wide range of services and at the same time elevating the fee of those services. Short Message Service (SMS) is considered one of the most commonly deployed messaging systems. However, this deployment has led to a spike in attacks on mobile devices such as SMS Spam. In this article, Artificial Intelligence (AI) method, which discovers and filters unsolicited spam messages was adopted. The machine learning algorithms used are Logistic Regression, Decision Trees, Gaussian Naïve Bayes; Multilayer perceptron (MLP) and Support Vector Machine (SVM). After experimentation, it was observed that MLP Classifier produced the best results with 93.1% true positive rate.
Description
Keywords
Spammed message; mobile application; artificial intelligence; attacks; SMS