Machine Learning Models for SMS Spam Detection

Title

Machine Learning Models for SMS Spam Detection

Creator

Basumatary, Baknai; Norbu, Tenzin Khetsuen; Kokatnoor, Sujatha Arun; Kumar, Sandeep

Description

With the increasing reliance on mobile communication, detecting spam messages sent via Short Messaging Service (SMS) has become more important. This advent has created a new era for spam in peoples lives, one that calls for quick attention and automatization in categorizing messages. This study analyzes three machine learning algorithmsLogistic Regression, Naive Bayes, and Decision Tree resulting in the binary classification of SMS messages into either spam or not spam (ham). To achieve effective spam detection, the study highlights the significance of feature engineering, model selection, and evaluation metrics such as accuracy, precision, recall, and F1-score. The research challenges, including unbalanced data, changing spam strategies, and the requirement for scalable solutions, are handled in this study. During experimentation, it was observed that Logistic Regression increased performance by 98.07% accuracy. The results also showed the advantages and disadvantages of each model, providing guidance on which strategy, is best for SMS spam filtering apps in the real world. This analysis aims to give readers a thorough grasp of existing approaches and how they might be used to improve the effectiveness and security of mobile communication systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1266 LNNS;pp.423-433

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Decision Trees; Ham; Logistic Regression; Machine learning; Nae Bayes; Random Forest; Short Messaging Service; Spam; Spam detection

Coverage

Basumatary B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Norbu T.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981962646-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-2647-2_29

https://www.scopus.com/pages/publications/105008647438?origin=resultslist