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Open AccessArticle

A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages

Department of Computer Science, Jouf University, Tabarjal 74728, Saudi Arabia
Higher School of Sciences and Technology of Hammam Sousse, University of Sousse, Hammam Sousse 4011, Tunisia
Department of Information and Technology Systems, Cairo University, Giza 12613, Egypt
Author to whom correspondence should be addressed.
Future Internet 2020, 12(9), 156;
Received: 24 August 2020 / Revised: 7 September 2020 / Accepted: 14 September 2020 / Published: 18 September 2020
(This article belongs to the Section Cybersecurity)
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%. View Full-Text
Keywords: SMS spam detection; deep learning; CNN; LSTM; SMS Classification SMS spam detection; deep learning; CNN; LSTM; SMS Classification
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Ghourabi, A.; Mahmood, M.A.; Alzubi, Q.M. A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages. Future Internet 2020, 12, 156.

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