An Improved ensemble deep learning model based on CNN for malicious website detection
A malicious website, also known as a phishing website, remains one of the major concerns in the cybersecurity domain. Among numerous deep learning-based solutions for phishing website detection, a Convolutional Neural Network (CNN) is one of the most popular techniques. However, when used as a stand...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
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2022
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| _version_ | 1796866797037682688 |
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| author | Do, Nguyet Quang Selamat, Ali Lim, Kok Cheng Krejcar, Ondrej |
| author_facet | Do, Nguyet Quang Selamat, Ali Lim, Kok Cheng Krejcar, Ondrej |
| author_sort | Do, Nguyet Quang |
| collection | ePrints |
| description | A malicious website, also known as a phishing website, remains one of the major concerns in the cybersecurity domain. Among numerous deep learning-based solutions for phishing website detection, a Convolutional Neural Network (CNN) is one of the most popular techniques. However, when used as a stand-alone classifier, CNN still suffers from an accuracy deficiency issue. Therefore, the main objective of this paper is to explore the hybridization of CNN with another deep learning algorithm to address this problem. In this study, CNN was combined with Bidirectional Gated Recurrent Unit (BiGRU) to construct an ensemble model for malicious webpage classification. The performance of the proposed CNN-BiGRU model was evaluated against several deep learning approaches using the same dataset. The results indicated that the proposed CNN-BiGRU is a promising solution for malicious website detection. In addition, ensemble architectures outperformed single models as they joined the advantages and cured the disadvantages of individual deep learning algorithms. |
| first_indexed | 2024-03-05T21:17:34Z |
| format | Conference or Workshop Item |
| id | utm.eprints-99649 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:17:34Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | utm.eprints-996492023-03-08T04:20:18Z http://eprints.utm.my/99649/ An Improved ensemble deep learning model based on CNN for malicious website detection Do, Nguyet Quang Selamat, Ali Lim, Kok Cheng Krejcar, Ondrej T Technology (General) A malicious website, also known as a phishing website, remains one of the major concerns in the cybersecurity domain. Among numerous deep learning-based solutions for phishing website detection, a Convolutional Neural Network (CNN) is one of the most popular techniques. However, when used as a stand-alone classifier, CNN still suffers from an accuracy deficiency issue. Therefore, the main objective of this paper is to explore the hybridization of CNN with another deep learning algorithm to address this problem. In this study, CNN was combined with Bidirectional Gated Recurrent Unit (BiGRU) to construct an ensemble model for malicious webpage classification. The performance of the proposed CNN-BiGRU model was evaluated against several deep learning approaches using the same dataset. The results indicated that the proposed CNN-BiGRU is a promising solution for malicious website detection. In addition, ensemble architectures outperformed single models as they joined the advantages and cured the disadvantages of individual deep learning algorithms. 2022 Conference or Workshop Item PeerReviewed Do, Nguyet Quang and Selamat, Ali and Lim, Kok Cheng and Krejcar, Ondrej (2022) An Improved ensemble deep learning model based on CNN for malicious website detection. In: 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, 19 - 22 July 2022, Kitakyushu, Japan. http://dx.doi.org/10.1007/978-3-031-08530-7_42 |
| spellingShingle | T Technology (General) Do, Nguyet Quang Selamat, Ali Lim, Kok Cheng Krejcar, Ondrej An Improved ensemble deep learning model based on CNN for malicious website detection |
| title | An Improved ensemble deep learning model based on CNN for malicious website detection |
| title_full | An Improved ensemble deep learning model based on CNN for malicious website detection |
| title_fullStr | An Improved ensemble deep learning model based on CNN for malicious website detection |
| title_full_unstemmed | An Improved ensemble deep learning model based on CNN for malicious website detection |
| title_short | An Improved ensemble deep learning model based on CNN for malicious website detection |
| title_sort | improved ensemble deep learning model based on cnn for malicious website detection |
| topic | T Technology (General) |
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