Despite the rapid evolution of Internet protocol-based messaging services, SMS still remains an indisputable communication service in our lives until today. For example, several businesses consider that text messages are more effective than e-mails. This is because 82% of SMSs are read within 5 min., but consumers only open one in four e-mails they receive. The importance of SMS for mobile phone users has attracted the attention of spammers. In fact, the volume of SMS spam has increased considerably in recent years with the emergence of new security threats, such as SMiShing. In this paper, we propose a hybrid deep learning model for detecting SMS spam messages. This detection model is based on the combination of two deep learning methods CNN and LSTM. It is intended to deal with mixed text messages that are written in Arabic or English. For the comparative evaluation, we also tested other well-known machine learning algorithms. The experimental results that we present in this paper show that our CNN-LSTM model outperforms the other algorithms. It achieved a very good accuracy of 98.37%.

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The growing use of SMS by businesses to communicate with their customers has made attackers more interested in smishing attacks. Smishing is a security attack that involves sending a fake SMS message in order to steal the personal credentials of mobile users. This kind of attack has become a serious cyber-security issue and has caused great financial losses for both people and businesses. In this article we propose a hybrid security model called "SM-Detector" aiming to detect smishing messages in mobile environments. To increase the efficiency of "SM-Detector," we have combined three different detection methods: (i) identification of malicious URLs, (ii) identification of suspected words, phone numbers and emails with regular expression analysis, and (iii) classification of messages using BERT-based algorithms to distinguish spam messages. "SM-Detector" also includes a mobile application allowing the user to check their SMS and report smishing messages. Its strength is that it can deal with mixed text messages written in Arabic or English. The experimental evaluation conducted on English and Arabic datasets showed a remarkable accuracy of 99.63%.