An analytical review of Email Spam Recognition Using Deep Learning

Email spam detection has become a serious issue in contemporary communication systems as a result of the proliferation of unwanted emails. Traditional methods often fall short of the ever- evolving tactics employed by spammers. Deep learning methods provide promising avenues to raise the effectiveness and precision of spam identi- fication systems. This paper looks at the current advancements in the usage of deep learning algo- rithms for email spam identification. We provide experimental data, review relevant literature, dis- cuss techniques, and render conclusions about the efficacy of deep learning in reducing unsolicited emails.

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Publication Details

Published
2024-11-22
DOI
https://doi.org/10.1109/aece62803.2024.10911051
Citations
1
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.78
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article

An analytical review of Email Spam Recognition Using Deep Learning

Dimple Tiwari, Bhoopesh Singh Bhati, Rajni Devi, Sonam Juneja
1 citations
Spam and Phishing Detection
0.78
article

An analytical review of Email Spam Recognition Using Deep Learning

Dimple Tiwari, Bhoopesh Singh Bhati, Rajni Devi, Sonam Juneja
article en
1 citations

Abstract

Email spam detection has become a serious issue in contemporary communication systems as a result of the proliferation of unwanted emails. Traditional methods often fall short of the ever- evolving tactics employed by spammers. Deep learning methods provide promising avenues to raise the effectiveness and precision of spam identi- fication systems. This paper looks at the current advancements in the usage of deep learning algo- rithms for email spam identification. We provide experimental data, review relevant literature, dis- cuss techniques, and render conclusions about the efficacy of deep learning in reducing unsolicited emails.

Chandigarh University (IN), Guru Gobind Singh Indraprastha University (IN), Indian Institute of Information Technology Allahabad (IN)
Openalex Percentile: Top 17%
Spam and Phishing Detection
0.78
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