Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm
Due to the increased frequency of phishing attacks, network security has gained the attention of researchers. In addition to this, large volumes of data are created every day, and these data include inappropriate and unrelated features that influence the accuracy of machine learning. There is theref...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/13/2823 |
| _version_ | 1797591834223968256 |
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| author | Gharbi Alshammari Majdah Alshammari Tariq S. Almurayziq Abdullah Alshammari Mohammad Alsaffar |
| author_facet | Gharbi Alshammari Majdah Alshammari Tariq S. Almurayziq Abdullah Alshammari Mohammad Alsaffar |
| author_sort | Gharbi Alshammari |
| collection | DOAJ |
| description | Due to the increased frequency of phishing attacks, network security has gained the attention of researchers. In addition to this, large volumes of data are created every day, and these data include inappropriate and unrelated features that influence the accuracy of machine learning. There is therefore a need for a robust method of detecting phishing threats and improving detection accuracy. In this study, three classifiers were applied to improve the accuracy of a detection algorithm: decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Selecting the relevant features improves the detection accuracy for a target class and determines the class label with the greatest probability. The proposed work clearly describes how feature selection using the Chaotic Dragonfly Algorithm provides more accurate results than all other baseline classifiers. It also indicates the appropriate classifier to be applied when detecting phishing websites. Three publicly available datasets were used to evaluate the method. They are reliable datasets for training the model and measuring prediction accuracy. |
| first_indexed | 2024-03-11T01:44:02Z |
| format | Article |
| id | doaj.art-74274a770069498890481424e578f57e |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T01:44:02Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-74274a770069498890481424e578f57e2023-11-18T16:23:57ZengMDPI AGElectronics2079-92922023-06-011213282310.3390/electronics12132823Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly AlgorithmGharbi Alshammari0Majdah Alshammari1Tariq S. Almurayziq2Abdullah Alshammari3Mohammad Alsaffar4Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDue to the increased frequency of phishing attacks, network security has gained the attention of researchers. In addition to this, large volumes of data are created every day, and these data include inappropriate and unrelated features that influence the accuracy of machine learning. There is therefore a need for a robust method of detecting phishing threats and improving detection accuracy. In this study, three classifiers were applied to improve the accuracy of a detection algorithm: decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Selecting the relevant features improves the detection accuracy for a target class and determines the class label with the greatest probability. The proposed work clearly describes how feature selection using the Chaotic Dragonfly Algorithm provides more accurate results than all other baseline classifiers. It also indicates the appropriate classifier to be applied when detecting phishing websites. Three publicly available datasets were used to evaluate the method. They are reliable datasets for training the model and measuring prediction accuracy.https://www.mdpi.com/2079-9292/12/13/2823phishing detectionnetwork securitydecision treeKNNSVMoptimization algorithms |
| spellingShingle | Gharbi Alshammari Majdah Alshammari Tariq S. Almurayziq Abdullah Alshammari Mohammad Alsaffar Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm Electronics phishing detection network security decision tree KNN SVM optimization algorithms |
| title | Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm |
| title_full | Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm |
| title_fullStr | Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm |
| title_full_unstemmed | Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm |
| title_short | Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm |
| title_sort | hybrid phishing detection based on automated feature selection using the chaotic dragonfly algorithm |
| topic | phishing detection network security decision tree KNN SVM optimization algorithms |
| url | https://www.mdpi.com/2079-9292/12/13/2823 |
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