Abstract :

The Rapid Growth Of Digital Communication Has Increased The Volume Of Unsolicited And Harmful Messages, Making Efficient Spam Detection Essential For Secure Information Exchange. This Work Presents An Intelligent Spam Detection System That Leverages Machine Learning Classification Techniques To Automatically Distinguish Legitimate Messages From Spam. The System Preprocesses Textual Data Through Cleaning, Tokenization, And Feature Extraction Using Approaches Such As TF-IDF And Word Embeddings. Multiple Classification Models, Including Naïve Bayes, Support Vector Machine, And Random Forest, Are Trained And Evaluated To Identify The Most Effective Algorithm For Accurate Spam Recognition. The Proposed System Demonstrates Strong Performance In Terms Of Precision, Recall, And Overall Accuracy, Reducing False Positives While Maintaining Reliable Detection Of Malicious Content. By Continuously Learning From New Data Patterns, The System Adapts To Evolving Spam Strategies, Ensuring Long-term Effectiveness. This Intelligent Approach Provides A Scalable And Automated Solution For Enhancing Communication Security Across Email Platforms, Messaging Applications, And Organizational Networks. Keywords:Spam Detection, Machine Learning, Email Filtering, Classification Techniques, Natural Language Processing (NLP), Feature Extraction, Naive Bayes, Support Vector Machine (SVM), Random Forest, Text Mining, Cybersecurity, Dataset Preprocessing, Automated Email Classification.

Published:

11-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

175-180

Section:

Articles

License:

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

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