Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification
Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7441 |
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| author | Hamed Alqahtani Saud S. Alotaibi Fatma S. Alrayes Isra Al-Turaiki Khalid A. Alissa Amira Sayed A. Aziz Mohammed Maray Mesfer Al Duhayyim |
| author_facet | Hamed Alqahtani Saud S. Alotaibi Fatma S. Alrayes Isra Al-Turaiki Khalid A. Alissa Amira Sayed A. Aziz Mohammed Maray Mesfer Al Duhayyim |
| author_sort | Hamed Alqahtani |
| collection | DOAJ |
| description | Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%. |
| first_indexed | 2024-03-09T05:37:54Z |
| format | Article |
| id | doaj.art-a161153c399f49cf942ef69d88514b39 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:37:54Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a161153c399f49cf942ef69d88514b392023-12-03T12:27:13ZengMDPI AGApplied Sciences2076-34172022-07-011215744110.3390/app12157441Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and ClassificationHamed Alqahtani0Saud S. Alotaibi1Fatma S. Alrayes2Isra Al-Turaiki3Khalid A. Alissa4Amira Sayed A. Aziz5Mohammed Maray6Mesfer Al Duhayyim7Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, King Saud University, P.O Box 145111, Riyadh 4545, Saudi ArabiaSAUDI ARAMCO Cybersecurity Chair, Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, EgyptDepartment of Information Systems, College of Computer Science, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaWebsite phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%.https://www.mdpi.com/2076-3417/12/15/7441cybersecurityinternet of thingscloud computingcomputational modelsdeep learningmetaheuristics |
| spellingShingle | Hamed Alqahtani Saud S. Alotaibi Fatma S. Alrayes Isra Al-Turaiki Khalid A. Alissa Amira Sayed A. Aziz Mohammed Maray Mesfer Al Duhayyim Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification Applied Sciences cybersecurity internet of things cloud computing computational models deep learning metaheuristics |
| title | Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification |
| title_full | Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification |
| title_fullStr | Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification |
| title_full_unstemmed | Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification |
| title_short | Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification |
| title_sort | evolutionary algorithm with deep auto encoder network based website phishing detection and classification |
| topic | cybersecurity internet of things cloud computing computational models deep learning metaheuristics |
| url | https://www.mdpi.com/2076-3417/12/15/7441 |
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