Machine Learning Techniques on Mobile SMS Spam Detection

DOI

10.2991/978-94-6463-716-8_7How to use a DOI?

Keywords

SMS Detection; Spam Detection; Machine Learning Algorithms Analysis; Natural Language Processing

Abstract

Unsolicited mass sms or fraudulent sms delivered to people or organisations are known as spam. To prevent data breaches and invasions of privacy, spam texts must be recognized and eliminated. Scholars are consistently investigating machine learning approaches and strategies to efficiently distinguish and categorise spam sms from authentic ones, often known as “ham” sms. Researchers have built systems that can accurately classify sms as spam or ham by analysing numerous textual elements. This study assesses the accuracy of several classification techniques in identifying spam from valid sms by analysing data gathered from multiple sources. sms are filtered and categorised using Natural Language Processing (NLP) algorithms according to their content. The Extreme Learning Machine (ELM) is one instance of a machine learning model used for this purpose. ELM is the state-of-the-art feedforward neural network technique with a single hidden layer. ELM avoids overfitting problems and has quick training times compared to standard neural networks. Because ELM only needs one iteration cycle, spam detection using it is both practical and efficient. This paper concludes by reviewing and contrasting a number of machine learning techniques for spam detection, emphasising the efficiency and adaptability of strategies like ELM in protecting against spam sms on a variety of domains.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Megha Birthare
AU  - Neelesh Jain
AU  - Alpana Meena
PY  - 2025
DA  - 2025/05/26
TI  - Machine Learning Techniques on Mobile SMS Spam Detection
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 73
EP  - 87
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_7
DO  - 10.2991/978-94-6463-716-8_7
ID  - Birthare2025
ER  -