Abstract :

In The Digital Era, The Widespread Use Of Mobile Communication Has Made Short Message Service (SMS) A Prime Target For Spammers And Cybercriminals. Spam Messages Not Only Disrupt User Experience But Often Serve As Vectors For Phishing Attacks, Malware Distribution, And Fraudulent Schemes. With The Proliferation Of Such Threats, There Is A Pressing Need For Intelligent Systems Capable Of Automatically Detecting And Filtering Spam Content To Safeguard Users From Potential Harm. This Project Presents A Hybrid Machine Learning Approach That Addresses Two Critical Tasks: SMS Spam Detection And URL Malicious Classification. The First Component Focuses On Classifying SMS Messages As Either Spam Or Ham (legitimate) Using Natural Language Processing (NLP) Techniques And Supervised Machine Learning Algorithms. Text Preprocessing Methods Such As Tokenization, Stopword Removal, And TF-IDF Vectorization Are Employed To Transform Raw SMS Text Into Meaningful Features Suitable For Model Training. The Second Component Targets The Classification Of URLs Embedded Within SMS Messages To Determine Whether They Are Malicious Or Benign. By Extracting Lexical Features—such As URL Length, Number Of Digits, Use Of Special Characters, And Domain-related Attributes—the System Utilizes Ensemble Classifiers Like Random Forest And XGBoost To Detect Suspicious URLs. This Dual-layered Detection Mechanism Enhances Security By Identifying Both Unsolicited Messages And Hidden Threats Within Them. Evaluation Of Both Models Was Performed Using Publicly Available Datasets, And The Results Demonstrated High Accuracy, Precision, And Recall, Proving The Effectiveness Of The Proposed Approach. The Integration Of Spam Detection With Malicious URL Classification Provides A More Robust Solution Compared To Traditional Standalone Filters, Significantly Reducing The Risk Of User Exploitation. Overall, This Project Contributes A Comprehensive Solution For Enhancing Digital Communication Security. It Can Be Deployed In Mobile Applications, Messaging Platforms, Or Enterprise Systems To Provide Real-time Protection Against Spam And Malicious Attacks, Thereby Fostering A Safer Messaging Ecosystem For Users.

Published:

30-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1274-1281

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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