# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 3 The Proposed Model  
In this section, we describe, in detail, our proposed model. The main idea of this detection system is to process the collected SMSs and apply a machine learning method to classify them and identify those that are considered to be spam or phishing messages. In Figure 1, we present the architecture of the proposed model. In this model, we have chosen to apply two directives of machine learning  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Paper** & **Approach Objective** & **Used Methods** & **Dataset Type** \\ \hline
[2] & Classify SMS and identify spam messages & CNN and LSTM & \(\bullet\) & UCI machine learning repository: SMS spam collection dataset [28] \\ \hline
[7] & Identify spam messages and inspect included URL & Naive Bayes & \(\bullet\) & UCI machine learning repository: SMS spam collection dataset [28] \\  & Classify SMS and identify spam messages & Naive Bayes & \(\bullet\) & Pinterest Smishing message images \\ \hline
[24] & Classify SMS and identify spam messages & Rule-based classification & \(\bullet\) & UCI machine learning repository: SMS spam collection dataset [28] \\ \hline
[22] & Classify SMS and identify spam messages & Random Forest, Decision Tree, Support Vector Machine and AdaBoost. & \(\bullet\) & UCI machine learning repository: SMS spam collection dataset [28] \\ \hline
[23] & Identify spam messages and inspect included URL & Feature-based technique, Random Forest, Naive Bayes, Support Vector Machine and Neural Network. & \(\bullet\) & UCI machine learning repository: SMS spam collection data set \\ \hline
[25] & Normalize and expand text messages to improve the classification performance & Lexicography, semantic dictionaries and techniques of semantic analysis and disambiguation & \(\bullet\) & UCI machine learning repository: SMS spam collection dataset [28] \\ \hline
[21] & Classify SMS and identify spam messages & FP-growth and Naive Bayes & \(\bullet\) & UCI machine learning repository: SMS spam collection dataset [28] \\ \hline
[26] & Classify SMS and identify spam messages & Bayesian learning and Support Vector Machine & \(\bullet\) & Indian SMS dataset \\ \hline
[27] & Classify SMS and identify spam messages & Multinomial Naive Bayes, Support Vector Machine, Random Forest and Adaboost & \(\bullet\) & UCI machine learning repository: SMS spam collection dataset [28] \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative Summary of related work.  
classification. The first is based on traditional machine learning algorithms, like Naive Bayes, SVM, Decision Tree, KNN, etc. The second directive is based on deep learning algorithms. The objective of this variety of algorithms is to try several classification methods in order to ultimately choose the one that gives the best results.  
The process of the proposed system begins by cleaning up unnecessary information found in the text messages. Subsequently, a pre-processing task will be applied to these messages to represent the textual data in a compatible form, which will be given as input to the machine learning methods. After completing the preparation of the data, the classification algorithms will be applied to this data in order to distinguish spam messages and those that are not spam.