Phishing Email Detection Model Using Deep Learning
Email phishing is a widespread cyber threat that can result in the theft of sensitive information and financial loss. It uses malicious emails to trick recipients into providing sensitive information or transferring money, often by disguising themselves as legitimate organizations or individuals. As...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/20/4261 |
| _version_ | 1797574049788854272 |
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| author | Samer Atawneh Hamzah Aljehani |
| author_facet | Samer Atawneh Hamzah Aljehani |
| author_sort | Samer Atawneh |
| collection | DOAJ |
| description | Email phishing is a widespread cyber threat that can result in the theft of sensitive information and financial loss. It uses malicious emails to trick recipients into providing sensitive information or transferring money, often by disguising themselves as legitimate organizations or individuals. As technology advances and attackers become more sophisticated, the problem of email phishing becomes increasingly challenging to detect and prevent. In this research paper, the use of deep learning techniques, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, recurrent neural networks (RNNs), and bidirectional encoder representations from transformers (BERT), are explored for detecting email phishing attacks. A dataset of phishing and benign emails was utilized, and a set of relevant features was extracted using natural language processing (NLP) techniques. The proposed deep learning model was trained and tested using the dataset, and it was found that it can achieve high accuracy in detecting email phishing compared to other state-of-the-art research, where the best performance was seen when using BERT and LSTM with an accuracy of 99.61%. The results demonstrate the potential of deep learning for improving email phishing detection and protecting against this pervasive threat. |
| first_indexed | 2024-03-10T21:17:44Z |
| format | Article |
| id | doaj.art-92312a777dde4af298f2ef99e993f322 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:17:44Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-92312a777dde4af298f2ef99e993f3222023-11-19T16:19:08ZengMDPI AGElectronics2079-92922023-10-011220426110.3390/electronics12204261Phishing Email Detection Model Using Deep LearningSamer Atawneh0Hamzah Aljehani1College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi ArabiaCollege of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi ArabiaEmail phishing is a widespread cyber threat that can result in the theft of sensitive information and financial loss. It uses malicious emails to trick recipients into providing sensitive information or transferring money, often by disguising themselves as legitimate organizations or individuals. As technology advances and attackers become more sophisticated, the problem of email phishing becomes increasingly challenging to detect and prevent. In this research paper, the use of deep learning techniques, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, recurrent neural networks (RNNs), and bidirectional encoder representations from transformers (BERT), are explored for detecting email phishing attacks. A dataset of phishing and benign emails was utilized, and a set of relevant features was extracted using natural language processing (NLP) techniques. The proposed deep learning model was trained and tested using the dataset, and it was found that it can achieve high accuracy in detecting email phishing compared to other state-of-the-art research, where the best performance was seen when using BERT and LSTM with an accuracy of 99.61%. The results demonstrate the potential of deep learning for improving email phishing detection and protecting against this pervasive threat.https://www.mdpi.com/2079-9292/12/20/4261email phishingconvolutional neural networks (CNNs)long short-term memory (LSTM)recurrent neural networks (RNNs)bidirectional encoder representations from transformers (BERT)deep learning |
| spellingShingle | Samer Atawneh Hamzah Aljehani Phishing Email Detection Model Using Deep Learning Electronics email phishing convolutional neural networks (CNNs) long short-term memory (LSTM) recurrent neural networks (RNNs) bidirectional encoder representations from transformers (BERT) deep learning |
| title | Phishing Email Detection Model Using Deep Learning |
| title_full | Phishing Email Detection Model Using Deep Learning |
| title_fullStr | Phishing Email Detection Model Using Deep Learning |
| title_full_unstemmed | Phishing Email Detection Model Using Deep Learning |
| title_short | Phishing Email Detection Model Using Deep Learning |
| title_sort | phishing email detection model using deep learning |
| topic | email phishing convolutional neural networks (CNNs) long short-term memory (LSTM) recurrent neural networks (RNNs) bidirectional encoder representations from transformers (BERT) deep learning |
| url | https://www.mdpi.com/2079-9292/12/20/4261 |
| work_keys_str_mv | AT sameratawneh phishingemaildetectionmodelusingdeeplearning AT hamzahaljehani phishingemaildetectionmodelusingdeeplearning |