Provide an Improved Model for Detecting Persian SMS Spam by Integrating Deep Learning and Machine Learning Models

Abstract

Spam is an example of unwanted content sent by unknown users and causing problems for mobile phone users. Disadvantages of spam include the inconvenience to the user, the loss of network traffic, the imposition of a calculation fee, the occupation of the physical space of the mobile phone, the misuse and fraud of the recipient. For this reason, the automatic detection of annoying text messages can be fundamental. Also, recognizing intelligently generated text messages is a challenge. Nevertheless, the current methods in this field face obstacles, such as the lack of appropriate Persian datasets. Experiences have shown that approaches based on deep and combined learning have better results in uncovering the annoying text messages. Accordingly, this study has attempted to provide an efficient method for detecting SMS spam by integrating machine learning classification algorithms and deep learning models. In the proposed method, after performing preprocessing on our collected dataset, two convolutional neural network layers and one LSTM layer and a fully connected layer are applied to extract the features are applied on the data which forms the deep learning part of the proposed method. The Support vector machine then utilizes the extracted information and features to perform the final classification, which is a part of the Machine Learning methods. The results show that the proposed model implements better than other algorithms and 97. 7% accuracy was achieved.