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Deep learning to filter SMS spam

Research output: Contribution to journalArticle

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Publication details

JournalFuture generation computer systems
DateAccepted/In press - 2 Sep 2019
DateE-pub ahead of print (current) - 4 Sep 2019
DatePublished - Jan 2020
Volume102
Number of pages10
Pages (from-to)524-533
Early online date4/09/19
Original languageEnglish

Abstract

The popularity of short message service (SMS) has been growing over the last decade. For businesses, these text messages are more effective than even emails. This is because while 98% of mobile users read their SMS by the end of the day, about 80% of the emails remain unopened. The popularity of SMS has also given rise to SMS Spam, which refers to any irrelevant text messages delivered using mobile networks. They are severely annoying to users. Most existing research that has attempted to filter SMS Spam has relied on manually identified features. Extending the current literature, this paper uses deep learning to classify Spam and Not-Spam text messages. Specifically, Convolutional Neural Network and Long Short-term memory models were employed. The proposed models were based on text data only, and self-extracted the feature set. On a benchmark dataset consisting of 747 Spam and 4,827 Not-Spam text messages, a remarkable accuracy of 99.44% was achieved.

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© 2019 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

    Research areas

  • spam detection, sms, machine learning, Deep learning, LSTM, convolutional neural network

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