Comprehensive study of sms spam detection strategies based on TCP protocols

Abstract

This study aims to enhance the accuracy of SMS spam detection, addressing the rising threat of unsolicited and potentially harmful messages in mobile communication. By leveraging both machine learning and deep learning techniques, we seek to identify the most effective model for distinguishing between spam and legitimate SMS messages. It uses a dataset consist of total 5572 number of data among 4825 non-spam and 747 was spam message. The dataset used consists of labeled SMS messages, which underwent preprocessing steps including cleaning, tokenization, and lowercasing. We implemented and evaluated several models: Logistic Regression, Multinomial Naive Bayes, Simple RNN, LSTM, and GRU. Among these, Logistic Regression outperformed all other models, achieving the highest accuracy. For machine learning, Multinomial Naive Bayes and Logistic Regression. Multinomial Naive Bayes has been trained in two ways. TF-IDF and CV (Count Vectorization). Among them, CV based MultinomialNB performed better than TF-IDF based MultinomialNB which is 97.68% compared to 95.85%. In deep learning, we choose RNN based models such as LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit) and SimpleRNN in order to achieve better accuracy. These deep learning models achieve accuracy of 97.78%, 97.78% and 97.97% respectively. Later Logistic Regression, another machine learning involves in this study outperformed all the models with an accuracy of 99.84% with precision of 99.69%. The findings highlight the robustness and efficiency of traditional ML models in handling the SMS spam detection task, while also providing insights into the performance of advanced DL models. This research contributes to the development of reliable and scalable spam detection solutions, enhancing user safety and the overall security of mobile communication systems.