Hybrid spam message detection using convolutional neural network and long short-term memory techniques

Abstract

Short Message Service (SMS) is a feature of a mobile phone that enable convenient and instant way of sending electronic messages between users. As SMS usage increases fraudulent text messages, known as spam, are becoming more common. Spam SMS may result in leaking personal information, invasion of privacy or accessing unauthorized data from mobile devices. Users of mobile phones can mistakingly give away personal information with the assumption that they are sharing it with the right recipients. This work propose a SMS spam detection method that combines convolutional neural network (CNN) and long short term memory (LSTM) deep learning algorithms. The CNN is used for feature extraction while the LSTM classifies the message. The SMS spam dataset, collected from online repository, is used to train the model. Word embeddings is used to vectorize the words in the message to make it suitable for the model. The result obtained from the implementation outperforms other machine learning algorithms with an accuracy of 99.77%.