Spam-Detection with Comparative Analysis and Spamming Words Extractions

  • Md Khairul Islam
  • , Md Al Amin
  • , Md Rakibul Islam
  • , Md Nosin Ibna Mahbub
  • , Md Imran Hossain Showrov
  • , Chetna Kaushal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

Communication through email plays an essential part especially in every sector of our day-to-day life. Considering its significance, it is important to filter spam emails from emails. Spam email, also known as junk email, is unwanted messages that are sent by the electronic medium in large quantities. Most of the spam emails are commercial in nature that is not only irritating but also harmful due to malicious scams or malware hosting sites or use viruses attached to the message. In this paper, we identify spam emails and expose how spam emails can be distinguished from legitimate/normal emails. We deployed four machine learning models and two deep learning models over the datasets including the combined dataset. Besides, we also try to find the important keywords that are found repeatedly from spam emails repository. This type of knowledge will enable us to detect spam emails for our personnel and community security purpose.

Original languageEnglish
Title of host publication2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665417037
DOIs
StatePublished - 2021
Externally publishedYes
Event9th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021 - Noida, India
Duration: 3 Sep 20214 Sep 2021

Publication series

Name2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

Conference

Conference9th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021
Country/TerritoryIndia
CityNoida
Period3/09/214/09/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Deep Learning
  • Machine Learning
  • NLP
  • Spam mail
  • Word Clouds
  • legitimate

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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