# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 1 Introduction  
SMS, the abbreviation for Short Message Service, is used as a channel for communication in mobile networks. It is the most commonly used service for data exchange in the world today. It has been determined that 8.3 trillion SMSs have been sent overall and approximately 69 billion SMS have been sent monthly across the world in recent years [1]. SMS spam means a deceptive text message sent through a mobile network with the aim of threatening users [2]. Today, SMS is regarded as a simple method for attackers in communicating with victims [3]. Smartphone users are potential victims of smishing and phishing attacks [4]. Attackers send a link or use SMS texts in order to contact users directly. They try to steal users' confidential information such as bank account details and credit card numbers through this method [5]. The SMS spam filter in mobile phones still has not achieved a sufficient level of protection compared to e-mails [6]. Today, deep learning algorithms are being used for the solution to the problems encountered in blocking SMS spam in mobile phones. This structure, using long short-term memory (LSTM), CNNs, and RNNs as deep neural networks, has been used in many classification problems regarding texts, images, and videos [7, 8]. Deep learning architectures are used for conventional machine learning algorithms such as clustering and automatic pattern recognition. Deep learning has multiple data processing layers [9]. Deep neural network (DNN) components are used in classifiers in order to minimize errors [10]. In addition, it is observed that the originality of URLs has been inspected for the classification of SMS spam messagesin recent years [11]. There are also studies through which test mediums were prepared with the intention of measuring the performance of spam detection tools that are currently used [12]. Researchers attained high performance through deep learning methods in resolving the issues of recognition and analysis of Turkish expressions such as Turkish handwritten text recognition [13; 14], Turkish text recognition in videos [15], automatic speech recognition [16], machine translation [17; 18], text categorization [19], and sentiment analysis [20; 21]. This study aimed to analyze SMS messages delivered in the Turkish or English languages. A classification was performed by using the model formed in the testing phase and messages. The challenges we encountered during the study were as follows. The first issue was designing a binary classification scheme for the operation of detecting spam messages within two different datasets holding data labeled as spam and non-spam. It was our intention to benefit from the power of deep neural networks while identifying SMS spam messages. The second issue was the low number of terms, which is one of the significant issues encountered in SMS spam classification. In this study, the number of terms was weighted. This enabled the calculation of the observation frequency of the relevant term. The higher the weight of a term, the greater the number of distinctive features of the relevant term for the relevant document. Our approach was based on a hybrid CNN + GRU method. CNNs deliver perfect performance in extracting n-gram features in data by using convolutional operations [22]. Gated recurrent units (GRUs), on the other hand, are able to process word indexes and capture long-term dependencies [23]. Accordingly, the design for a hybrid model was laid out in a way that combined these two algorithms. It was revealed by the results obtained that the highest accuracy value was attained by the hybrid CNN + GRU approach, with 99.07%. The innovative aspect of our study is the hybrid CNN + GRU model, which was used in the detection of Turkish SMS spam. The distinction of this study is the first-time usage of the proposed hybrid CNN + GRU model for Turkish SMS spam detection. Furthermore, as clarified in the proposed model section, the hyperparameters used in the approach are different from those in other studies.