Enhanced Security and Reliability in Messaging Systems through Real-Time SMS Spam Filtering with Machine Learning

Abstract— SMS spam has emerged as a major issue for cellular users, which leads to annoyance and inconvenience. Machine learning has been effective in filtering out spam SMS. Yet, applying these techniques in actual real-time situations poses special challenges. A newly published study seeks to tackle these challenges by building an efficient real-time. SMS spam filtering system based on machine learning. The main contribution of this work is to improve the performance of the system in real-time classification by focusing on data preparation, feature engineering, algorithm selection, and model deployment. Keywords—SMS spam filtering, Real-time classification, Machine Learning

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-05-31
DOI
https://doi.org/10.55041/ijsrem48995
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhanced Security and Reliability in Messaging Systems through Real-Time SMS Spam Filtering with Machine Learning

S. Mounika
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Spam and Phishing Detection
article

Enhanced Security and Reliability in Messaging Systems through Real-Time SMS Spam Filtering with Machine Learning

S. Mounika
article en

Abstract

Abstract— SMS spam has emerged as a major issue for cellular users, which leads to annoyance and inconvenience. Machine learning has been effective in filtering out spam SMS. Yet, applying these techniques in actual real-time situations poses special challenges. A newly published study seeks to tackle these challenges by building an efficient real-time. SMS spam filtering system based on machine learning. The main contribution of this work is to improve the performance of the system in real-time classification by focusing on data preparation, feature engineering, algorithm selection, and model deployment. Keywords—SMS spam filtering, Real-time classification, Machine Learning

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(05)
Openalex Percentile: Top 83%
Spam and Phishing Detection
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