A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection
Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are som...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10260 |
| _version_ | 1827727407867494400 |
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| author | Abed Alanazi Abdu Gumaei |
| author_facet | Abed Alanazi Abdu Gumaei |
| author_sort | Abed Alanazi |
| collection | DOAJ |
| description | Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and approaches proposed in the state-of-the-art studies, the advancement of the most effective solution is still of research interest and needs to be improved. Recently, decision fusion methods play an important role in improving the accuracy of ML methods. They are broadly classified based on the type of fusion into a voting decision fusion technique and a divide and conquer decision fusion technique. In this paper, a decision fusion ensemble learning (DFEL) model is proposed based on voting technique for detecting malicious websites. It combines the predictions of three effective ensemble classifiers, namely, gradient boosting (GB) classifier, extreme gradient boosting (XGB) classifier, and random forest (RF) classifier. We use these classifiers because their advantages to perform well for class imbalanced and data with statistical noises such as in the case of malicious websites detection. A weighted majority-voting rule is utilized for generating the final decisions of used classifiers. The experimental results are conducted on a publicly available large dataset of malicious and benign websites. The comparative study exposed that the DFEL model achieves high accuracies, which are 97.25% on average of 10-fold cross-validation test and 98.50% on a holdout of 30% test set. This confirms the ability of proposed approach to improve the detection rate of malicious websites. |
| first_indexed | 2024-03-10T23:04:45Z |
| format | Article |
| id | doaj.art-f1ae4260186749538371a60cd1ca7f9c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:04:45Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f1ae4260186749538371a60cd1ca7f9c2023-11-19T09:25:15ZengMDPI AGApplied Sciences2076-34172023-09-0113181026010.3390/app131810260A Decision-Fusion-Based Ensemble Approach for Malicious Websites DetectionAbed Alanazi0Abdu Gumaei1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaMalicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and approaches proposed in the state-of-the-art studies, the advancement of the most effective solution is still of research interest and needs to be improved. Recently, decision fusion methods play an important role in improving the accuracy of ML methods. They are broadly classified based on the type of fusion into a voting decision fusion technique and a divide and conquer decision fusion technique. In this paper, a decision fusion ensemble learning (DFEL) model is proposed based on voting technique for detecting malicious websites. It combines the predictions of three effective ensemble classifiers, namely, gradient boosting (GB) classifier, extreme gradient boosting (XGB) classifier, and random forest (RF) classifier. We use these classifiers because their advantages to perform well for class imbalanced and data with statistical noises such as in the case of malicious websites detection. A weighted majority-voting rule is utilized for generating the final decisions of used classifiers. The experimental results are conducted on a publicly available large dataset of malicious and benign websites. The comparative study exposed that the DFEL model achieves high accuracies, which are 97.25% on average of 10-fold cross-validation test and 98.50% on a holdout of 30% test set. This confirms the ability of proposed approach to improve the detection rate of malicious websites.https://www.mdpi.com/2076-3417/13/18/10260cyber-securitymalicious websitesbenign websitesURLsensemble learningdecision fusion ensemble learning (DFEL) model |
| spellingShingle | Abed Alanazi Abdu Gumaei A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection Applied Sciences cyber-security malicious websites benign websites URLs ensemble learning decision fusion ensemble learning (DFEL) model |
| title | A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection |
| title_full | A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection |
| title_fullStr | A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection |
| title_full_unstemmed | A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection |
| title_short | A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection |
| title_sort | decision fusion based ensemble approach for malicious websites detection |
| topic | cyber-security malicious websites benign websites URLs ensemble learning decision fusion ensemble learning (DFEL) model |
| url | https://www.mdpi.com/2076-3417/13/18/10260 |
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