Malicious URL Detection with Distributed Representation and Deep Learning
Computer crime; Convolutional neural networks; Embeddings; Natural language processing systems; Recurrent neural networks; Character level; Convolutional neural network; Deep learning; Distributed representation; Embeddings; Learning models; Malicious URL; Natural languages; Phishing detections; Wor...
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IOS Press BV
2023
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my.uniten.dspace-267472023-05-29T17:36:29Z Malicious URL Detection with Distributed Representation and Deep Learning Do N.Q. Selamat A. Lim K.C. Krejcar O. 57283917100 24468984100 57889660500 14719632500 Computer crime; Convolutional neural networks; Embeddings; Natural language processing systems; Recurrent neural networks; Character level; Convolutional neural network; Deep learning; Distributed representation; Embeddings; Learning models; Malicious URL; Natural languages; Phishing detections; Word level; Websites There exist numerous solutions to detect malicious URLs based on Natural Language Processing and machine learning technologies. However, there is a lack of comparative analysis among approaches using distributed representation and deep learning. To solve this problem, this paper performs a comparative study on phishing URL detection based on text embedding and deep learning algorithms. Specifically, character-level and word-level embedding were combined to learn the feature representations from the webpage URLs. In addition, three deep learning models, including Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM), were constructed for effective classification of phishing websites. Several experiments were conducted and various evaluation metrics were used to assess the performance of these deep learning models. The findings obtained from the experiments indicated that the combination of the character-level and word-level embedding approach produced better results than the individual text representation methods. Also, the CNN-based model outperformed the other two deep learning algorithms in terms of both detection accuracy and execution time. � 2022 The authors and IOS Press. All rights reserved. Final 2023-05-29T09:36:29Z 2023-05-29T09:36:29Z 2022 Conference Paper 10.3233/FAIA220248 2-s2.0-85139801300 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139801300&doi=10.3233%2fFAIA220248&partnerID=40&md5=5f50d13b144b88a47aa50e591c4c048f https://irepository.uniten.edu.my/handle/123456789/26747 355 171 180 IOS Press BV Scopus |
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| description |
Computer crime; Convolutional neural networks; Embeddings; Natural language processing systems; Recurrent neural networks; Character level; Convolutional neural network; Deep learning; Distributed representation; Embeddings; Learning models; Malicious URL; Natural languages; Phishing detections; Word level; Websites |
| author2 |
57283917100 |
| author_facet |
57283917100 Do N.Q. Selamat A. Lim K.C. Krejcar O. |
| format |
Conference Paper |
| author |
Do N.Q. Selamat A. Lim K.C. Krejcar O. |
| spellingShingle |
Do N.Q. Selamat A. Lim K.C. Krejcar O. Malicious URL Detection with Distributed Representation and Deep Learning |
| author_sort |
Do N.Q. |
| title |
Malicious URL Detection with Distributed Representation and Deep Learning |
| title_short |
Malicious URL Detection with Distributed Representation and Deep Learning |
| title_full |
Malicious URL Detection with Distributed Representation and Deep Learning |
| title_fullStr |
Malicious URL Detection with Distributed Representation and Deep Learning |
| title_full_unstemmed |
Malicious URL Detection with Distributed Representation and Deep Learning |
| title_sort |
malicious url detection with distributed representation and deep learning |
| publisher |
IOS Press BV |
| publishDate |
2023 |
| _version_ |
1806426543156625408 |
| score |
13.214268 |