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Email Phishing Detection Model using CNN Model

Phishing is the most common cybercrime tactic that convinces victims to divulge sensitive information, including passwords, account IDs, sensitive bank information, and dates of birth. Cybercriminals commonly use phone calls, text messages, and emails to launch these kinds of attacks. Despite con...

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Main Authors:	Gurumurthy, M., Chitra, K
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.intimal.edu.my/2093/1/joit2024_43.pdf http://eprints.intimal.edu.my/2093/2/632 http://eprints.intimal.edu.my/2093/ http://ipublishing.intimal.edu.my/joint.html
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id	my-inti-eprints.2093
record_format	eprints
spelling	my-inti-eprints.20932024-12-12T09:23:43Z http://eprints.intimal.edu.my/2093/ Email Phishing Detection Model using CNN Model Gurumurthy, M. Chitra, K QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Phishing is the most common cybercrime tactic that convinces victims to divulge sensitive information, including passwords, account IDs, sensitive bank information, and dates of birth. Cybercriminals commonly use phone calls, text messages, and emails to launch these kinds of attacks. Despite continuous reworking of the tactics to keep a safe distance from these cyberattacks, the severe outcome is currently absent. However, in recent years, the number of phishing emails has increased dramatically, indicating the need for more advanced and effective ways to combat them. Although several tactics have been put in place to divert phishing emails, a comprehensive solution is still required. To the best of our knowledge, this is the first study to focus on using machine learning (ML) and natural language processing (NLP) techniques to identify phishing emails. With a focus on machine learning techniques, this research examines the many NLP techniques now in use to identify phishing emails at various stages of the attack. These methods are investigated and their comparative assessment is made. This provides an overview of the problem, its immediate workspace, and the expected implications for further research. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2093/1/joit2024_43.pdf text en cc_by_4 http://eprints.intimal.edu.my/2093/2/632 Gurumurthy, M. and Chitra, K (2024) Email Phishing Detection Model using CNN Model. Journal of Innovation and Technology, 2024 (43). pp. 1-8. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English English
topic	QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Gurumurthy, M. Chitra, K Email Phishing Detection Model using CNN Model
description	Phishing is the most common cybercrime tactic that convinces victims to divulge sensitive information, including passwords, account IDs, sensitive bank information, and dates of birth. Cybercriminals commonly use phone calls, text messages, and emails to launch these kinds of attacks. Despite continuous reworking of the tactics to keep a safe distance from these cyberattacks, the severe outcome is currently absent. However, in recent years, the number of phishing emails has increased dramatically, indicating the need for more advanced and effective ways to combat them. Although several tactics have been put in place to divert phishing emails, a comprehensive solution is still required. To the best of our knowledge, this is the first study to focus on using machine learning (ML) and natural language processing (NLP) techniques to identify phishing emails. With a focus on machine learning techniques, this research examines the many NLP techniques now in use to identify phishing emails at various stages of the attack. These methods are investigated and their comparative assessment is made. This provides an overview of the problem, its immediate workspace, and the expected implications for further research.
format	Article
author	Gurumurthy, M. Chitra, K
author_facet	Gurumurthy, M. Chitra, K
author_sort	Gurumurthy, M.
title	Email Phishing Detection Model using CNN Model
title_short	Email Phishing Detection Model using CNN Model
title_full	Email Phishing Detection Model using CNN Model
title_fullStr	Email Phishing Detection Model using CNN Model
title_full_unstemmed	Email Phishing Detection Model using CNN Model
title_sort	email phishing detection model using cnn model
publisher	INTI International University
publishDate	2024
url	http://eprints.intimal.edu.my/2093/1/joit2024_43.pdf http://eprints.intimal.edu.my/2093/2/632 http://eprints.intimal.edu.my/2093/ http://ipublishing.intimal.edu.my/joint.html
_version_	1818840135380238336
score	13.223943

Email Phishing Detection Model using CNN Model

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