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

Author

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  • Abdallah Ghourabi

    (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)

  • Mahmood A. Mahmood

    (Department of Computer Science, Jouf University, Tabarjal 74728, Saudi Arabia
    Department of Information and Technology Systems, Cairo University, Giza 12613, Egypt)

  • Qusay M. Alzubi

    (Department of Computer Science, Jouf University, Tabarjal 74728, Saudi Arabia)

Abstract

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%.

Suggested Citation

  • Abdallah Ghourabi & Mahmood A. Mahmood & Qusay M. Alzubi, 2020. "A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages," Future Internet, MDPI, vol. 12(9), pages 1-16, September.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:9:p:156-:d:415307
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    References listed on IDEAS

    as
    1. Jae Woong Joo & Seo Yeon Moon & Saurabh Singh & Jong Hyuk Park, 2017. "S-Detector: an enhanced security model for detecting Smishing attack for mobile computing," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(1), pages 29-38, September.
    2. Ankit Kumar Jain & Sumit Kumar Yadav & Neelam Choudhary, 2020. "A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques," International Journal of E-Services and Mobile Applications (IJESMA), IGI Global, vol. 12(1), pages 21-38, January.
    3. Mohamed Elleuch & Monji Kherallah, 2016. "An Improved Arabic Handwritten Recognition System using Deep Support Vector Machines," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 7(2), pages 1-20, April.
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