Spam SMS detection based on long short-term memory and recurrent neural network

Marya Jehad; Ali Bou Nassif; Mohammad AlShabi

doi:10.1117/12.2674147

12 June 2023 Spam SMS detection based on long short-term memory and recurrent neural network

Marya Jehad, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125381Y (2023) https://doi.org/10.1117/12.2674147
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

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Abstract

Short Messaging Service (SMS) becomes a more easy, affordable way to communicate and increasingly replace phone calls. Spam is any kind of unwanted, random unsolicited message that gets sent without any authorization from the receiver. hackers use spam SMS to get their important information. Effective spam detection is an essential tool for assisting users in determining whether an SMS is a spam or not. Different machine learning methods such as Deep Learning techniques have attempted to distinguish between spam and ham SMS texts. This paper proposes the Spam-Ham Classification method using Recurrent Neural Networks (RNN) and Long Short-Term Memories (LSTM). The proposed model utilizes Keras and TensorFlow to detect Spam SMS. The dataset used is SpamSMSCollection from the UCI machine learning repository. The dataset contains a set of 5574 SMS messages. The dataset is preprocessed using tokenization, Lemmatization, padding, and stopword removal. The overall accuracy of the proposed model is 98%. The performance of the proposed method is compared with different machine learning algorithms such as Support Vector Machine (SVM), K-nearest neighbors (KNN), and Multi-layer Perceptron (MLP).

Citation Download Citation

Marya Jehad, Ali Bou Nassif, and Mohammad AlShabi "Spam SMS detection based on long short-term memory and recurrent neural network", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125381Y (12 June 2023); https://doi.org/10.1117/12.2674147

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