Robust Machine Learning Technique for Detection and Classification of Spam Mails

EasyChair Preprint 10478

Abstract

 A massive number of spam mails have become difficulty for Internet users. Spammers can collect data by creating fake URLs, fake websites and fake chat rooms. Spam mails may lead to harassing, bullying or social traumatizing situations. To overcome this drawback in email   architecture, it is essential to boost up the existing technology and lay a stone for new outcomes. Spam mails can also be filtered using URLs, but this will lead to error prone. To solve these problems, several models have been existed and tested but none of those models achieved high accuracy. In this research work, a new method is proposed with the support of NLP with Machine learning and achieved 98.5% of accuracy on SMS Spam Collection dataset.

Keyphrases: Decision Tree, Ham, NLP, Random Forest, Spam, Stacking Classifier, XGBoost, machine learning

@booklet{EasyChair:10478,
  author    = {B Aruna Kumari and C Nagaraju},
  title     = {Robust Machine Learning Technique for Detection and Classification of Spam Mails},
  howpublished = {EasyChair Preprint 10478},
  year      = {EasyChair, 2023}}