Publication:
Malicious URL Detection with Distributed Representation and Deep Learning

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dc.contributor.authorDo N.Q.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorLim K.C.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57283917100en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid57889660500en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T09:36:29Z
dc.date.available2023-05-29T09:36:29Z
dc.date.issued2022
dc.descriptionComputer 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; Websitesen_US
dc.description.abstractThere 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/FAIA220248
dc.identifier.epage180
dc.identifier.scopus2-s2.0-85139801300
dc.identifier.spage171
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85139801300&doi=10.3233%2fFAIA220248&partnerID=40&md5=5f50d13b144b88a47aa50e591c4c048f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26747
dc.identifier.volume355
dc.publisherIOS Press BVen_US
dc.sourceScopus
dc.sourcetitleFrontiers in Artificial Intelligence and Applications
dc.titleMalicious URL Detection with Distributed Representation and Deep Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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