Email Spam Detection Using Machine Learning

Email spam has become a major problem in the modern world as a result of the sharp rise in internet users. These emails are frequently used for unethical and illegal purposes, such as fraud and phishing. Through these emails, spammers disseminate dangerous links that have the potential to compromise and harm our systems. Spammers can pretend to be real people in their spam messages by creating phony email accounts and profiles with ease. They typically prey on those who are not aware of these frauds. Therefore, being able to spot phony spam emails is essential. The goal of this project is to use machine learning techniques to identify such spam. Several machine learning algorithms will be examined in this paper, applied to our datasets, and the best algorithm will be selected.

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Publication Details

Published
2025-09-19
DOI
https://doi.org/10.38124/ijisrt/25jul1755
Citations
1
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
3.25
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article

Email Spam Detection Using Machine Learning

N Chetan, J Surya, Vinay Krishnamurthy, V Yogananda
1 citations
Spam and Phishing Detection
3.25
article

Email Spam Detection Using Machine Learning

N Chetan, J Surya, Vinay Krishnamurthy, V Yogananda
article en
1 citations

Abstract

Email spam has become a major problem in the modern world as a result of the sharp rise in internet users. These emails are frequently used for unethical and illegal purposes, such as fraud and phishing. Through these emails, spammers disseminate dangerous links that have the potential to compromise and harm our systems. Spammers can pretend to be real people in their spam messages by creating phony email accounts and profiles with ease. They typically prey on those who are not aware of these frauds. Therefore, being able to spot phony spam emails is essential. The goal of this project is to use machine learning techniques to identify such spam. Several machine learning algorithms will be examined in this paper, applied to our datasets, and the best algorithm will be selected.

Openalex Percentile: Top 6%
Spam and Phishing Detection
3.25
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