# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 1 Introduction  
Human uses short message service (SMS) in mobile as a way of communication or business. Recently SMS is the most used data service in the world. The world sent 8.3 trillion SMS messages in the year 2017, the number of SMS messages sent monthly is 690 billion, so SMS is important for business communications [1]. Recently, SMS spam target mobile phones. SMS spam refers to any illusion text message that is delivered using the mobile network. They are disturbing to users [2]. A survey exposes that 68% of mobile phone users are affected by SMS Spam [3]. In some cases, SMS spam contains malicious activities, such as smishing. Smishing is a cyber-security attack for mobile user aimed at deceiving the user via SMS spam messages that may include a link or malicious software or both. Smishing is combined of two words: SMS and Phishing [3]. Nowadays, attackers find the sms is a simple way to communicate with victims [4]. The most victims of smishig and phishing attacks are users that have a smartphone [5]. The attackers attempt to steal user's secret information, like credit card number, bank account details, etc., by sending a link or direct contact with victims by SMS texts [6]. In addition, the filtration of SMS spam in smartphones is still not very robust as compared to email spams that are supported by advanced methods of spam filtering [7]. Among the recent solutions that have proven to be effective in solving these kinds of problems is the use of deep neuralnetworks. Deep neural network-based architecture, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), have been used for many classification problems for images, videos and texts. Deep learning is used for automatic pattern recognition as well as traditional clustering machine learning techniques (unsupervised techniques). It has several layers of information processing stages [2]. The components of deep neural network are employed in all stages of deep learning classifiers to minimize the classification errors. However, deep neural networks are not yet well exploited in the classification of SMS messages. In the context of Arabic NLP, Deep Learning Models have proven their capabilities in several areas, like handwritten Arabic text recognition [8; 9; 10], recognition of Arabic text overlaid in videos [11], Automatic Speech Recognition [12], Machine translation [13; 14], Text Categorization [15], and Sentiment Analysis [16; 17; 18]. In this paper, we want to focus on the SMS messages that are delivered in the Arabic speaking countries. These messages are generally written in Arabic or English. Thus, the challenge that we face is: (i) how to collect a significant dataset supporting both Arabic and English language and allowing researchers to conduct studies on SMS spam; and, (ii) how to find a robust classification model to detect spam messages in this mixed environment. For the first problem, we collected a set of 2730 Arabic messages from several local smartphones in Saudi Arabia and tagged them as spam and not-spam. For the second problem, we chose to harness the strength of deep neural networks to identify SMS spam. The proposed model is based on a hybrid deep learning architecture combining the CNN and LSTM algorithms. CNN performs excellently in extracting n-gram features at different positions of a message through the use of convolutional operations [19] and, hence, can be useful to identify the most common words in spam messages. However, LSTM is able to handle word sequences of any length and capture long-term dependencies [19]. This can be useful for identifying dependencies between words in a spam message and, hence, LSTM can determine whether the message is spam or not from the initial words of the message. We designed a hybrid model combining these two algorithms in order to benefit from the advantages of both CNN and LSTM. In addition, to compare this model with other classification techniques, we tested several known machine learning algorithms.