A Multi-Channel Spam Detection System Utilizing Natural Language Processing and Machine Learning

Mohini Tyagi; Pradeep Kumar Singh; Shivam Kumar Yadav; Sanjay Kumar Soni

doi:10.4108/airo.8309

Authors

Mohini Tyagi Madan Mohan Malaviya University of Technology https://orcid.org/0009-0000-4661-3767
Pradeep Kumar Singh Madan Mohan Malaviya University of Technology
Shivam Kumar Yadav Madan Mohan Malaviya University of Technology
Sanjay Kumar Soni Madan Mohan Malaviya University of Technology

DOI:

https://doi.org/10.4108/airo.8309

Keywords:

Bidirectional Encoder Representations from Transformers, BERT, Machine Learning, ML, Natural Language Processing, NLP, Spam/Ham, Support Vector Machine, SVM

Abstract

As digital communication rapidly expands, the issue of unsolicited and unwanted messages, commonly known as spam, has become a major concern. This paper introduces an advanced spam detection system that integrates Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system differentiates between spam and legitimate messages by employing a hybrid model that combines Naive Bayes, Support Vector Machines (SVM), and deep learning models like Bidirectional Encoder Representations from
Transformers (BERT). The model demonstrates high effectiveness across various communication platforms, including emails, SMS, and social media, achieving an accuracy exceeding 98.5%.

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