# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 2 Related Work  
In recent years, several researchers have proposed solutions to identify SMS spam. A significant number of these works are based on the use of machine learning and datamining techniques. Some researchers have proposed the use of naive bayes algorithm to classify text messages as spam or ham [7; 20; 21]. Other researchers have tried other classifiers, such as Random Forest, Decision Tree, Support Vector Machine, and AdaBoost [22; 23], or even a rule-based classification [24]. Other researchers have been interested in standardizing and expanding the content of the messages to improve the classifiers performance [25]. Additionally, recently, researchers began using deep learning techniques for this task [2]. In [7], the authors proposed a model called \"Smishing Detector\" in order to detect smishing messages with a reduced false positive rate. The proposed model contains four modules. The goal of the first module is to analyze the content of text messages and identify the malicious contents by the use of Naive Bayes classification algorithm. The second one is used to inspect the URL contained in the messages. The third module is dedicated to analyze the source code of the website linked in the messages. The last module is an APK download detector, its role is to identify if a malicious file is downloaded when the URL is called. The experimental tests that were carried out by the authors on this model gave an accuracy of 96.29%. In recent paper [2], [PERSON] et al. proposed the use of deep learning to classify Spam and Not-Spam SMS messages. The objective of their approach is to employ two deep learning techniques together: CNN and LSTM. The idea is to classify text messages and identify those that are spam and those that are not spam. In order to evaluate the performance of the proposed approach, they compared it with other machine learning algorithms, like Naive Bayes, Random Forest, Gradient Boosting, Logistic Regression, and Stochastic Gradient Descent. The obtained results showed that the CNN and LSTM model is much better when compared to other machine learning models. In another work that was designed to detect smishing messages, [PERSON] et al. [20] proposed a model, called \"S-Detector\". This model employs four modules: a component to monitor the SMS activities, an analyzer to analyze the content of the SMS, a determinant to classify and blocks Smishing text messages, and a database to store the SMS data. Naive Bayes is the classification algorithm used in this model. In another paper [24], [PERSON] and [PERSON] proposed a rule-based classification technique to detect phishing SMS. Their approach has identified nine rules that can identify and filter phishing SMS from legitimate ones. Experimental tests that were conducted by the authors yielded 99% of true negative rate and 92% of true positive rate. In [22], [PERSON] et al. proposed a model, called \"SmiDCA\", for the detection of smishing messages based on machine learning algorithms. In the model, the authors chose to use correlation algorithms to extract the 39 most relevant features from smishing messages. Subsequently, they applied four machine learning classifiers to evaluate the performance of their model. The four classifiers were: Random Forest, Decision Tree, Support Vector Machine, and AdaBoost. The experimental evaluation of this model showed an accuracy of 96.4% with Random Forest Classifier. In [23], the authors proposed a feature-based approach to detect smishing messages. This approach extracts ten features that the authors claim to be able to distinguish false messages from ham. Subsequently, the features were implemented on benchmarked dataset with the use of five classification algorithms to judge the performance of the proposed approach. The experimental evaluation showed that the model can detect smishing messages with 94.20% of true positive rate and 98.74% overall accuracy. In [25], [PERSON] et al.