Abstract: The proliferation of mobile users has led to a significant increase in mobile messaging, resulting in a rise in SMS (Short Message Service) spam. Unlike other messaging platforms such as Facebook and Whats-app, SMS does not necessitate an active internet connection. Spam SMS messages, which are unwanted and potentially harmful to users, pose a substantial challenge in mobile communication. These messages are primarily aimed at distributing electronic messages for commercial or financial gain. Consequently, combating SMS spam is crucial for preserving the integrity of mobile communication channels. However, existing email filtering algorithms may underperform due to factors such as the lack of real databases for SMS spam, limited features, and informal. This study proposes an approach utilizing Machine Learning techniques to address SMS spam. The approach encompasses various components, including data-set combinations, data cleaning, exploratory data analysis, and feature engineering. Additionally, several machine learning algorithms, such as Naive Bayes and Support Vector Machine, are assessed for model building. The ultimate aim of SMS spam detection is to protect users from spam-related issues.
Keywords: Spam SMS, Facebook, Whats-app, Internet Connection, Financial gain, Data-sets, Data cleaning, Feature engineering, Naive Bayes, Model building.
Cite:
Shreya Menthe, Kanish Rawal, Mrudula Hirave, A. J. Patil,"SMS SPAM DETECTION USING MACHINE LEARNING", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13307.
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DOI:
10.17148/IJARCCE.2024.13307
