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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2093/ http://eprints.intimal.edu.my/2093/2/632 http://eprints.intimal.edu.my/2093/3/joit2024_43b.pdf |
| _version_ | 1848766918120439808 |
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| author | Gurumurthy, M. Chitra, K |
| author_facet | Gurumurthy, M. Chitra, K |
| author_sort | Gurumurthy, M. |
| building | INTI Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:58:46Z |
| format | Article |
| id | intimal-2093 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:46Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20932025-07-12T03:45:37Z 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/2093/2/632 text en cc_by_4 http://eprints.intimal.edu.my/2093/3/joit2024_43b.pdf 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 |
| 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 |
| title | 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_short | Email Phishing Detection Model using CNN Model |
| title_sort | email phishing detection model using cnn model |
| topic | QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.intimal.edu.my/2093/ http://eprints.intimal.edu.my/2093/ http://eprints.intimal.edu.my/2093/2/632 http://eprints.intimal.edu.my/2093/3/joit2024_43b.pdf |