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

Abdallah Ghourabi, Mahmood A. Mahmood and Qusay M. Alzubi
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Abdallah Ghourabi: Department of Computer Science, Jouf University, Tabarjal 74728, Saudi Arabia
Mahmood A. Mahmood: Department of Computer Science, Jouf University, Tabarjal 74728, Saudi Arabia
Qusay M. Alzubi: Department of Computer Science, Jouf University, Tabarjal 74728, Saudi Arabia

Future Internet, 2020, vol. 12, issue 9, 1-16

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%.

Keywords: SMS spam detection; deep learning; CNN; LSTM; SMS Classification (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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