Deep learning to filter SMS Spam

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.

The Impact of Deep Learning Techniques on SMS Spam Filtering

“…Table 3 and Fig. 10 provide a contrast between RMDL's accuracy and the best accuracy in the four deep learning articles [8][9][10][11] listed in the related work section. Using complicated 3CNN architecture in [8], the best accuracy was achieved.…”

“…In addition, some research has been presented in [8][9][10][11] on deep learning approaches for the detection of SMS spam. Using text information only, CNN and LSTM were tested in [8]. On both balanced and imbalanced datasets, the experiments were performed.…”

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

“…Other researchers have been interested in standardizing and expanding the content of the messages to improve the classifiers performance [25]. Additionally, recently, researchers began using deep learning techniques for this task [2].…”

“…SMS spam refers to any illusion text message that is delivered using the mobile network. They are disturbing to users [2]. A survey exposes that 68% of mobile phone users are affected by SMS Spam [3].…”

“…Deep learning is used for automatic pattern recognition as well as traditional clustering machine learning techniques (unsupervised techniques). It has several layers of information processing stages [2]. The components of deep neural network are employed in all stages of deep learning classifiers to minimize the classification errors.…”