Accurate and fast URL phishing detector: A convolutional neural network approach

Along with the development of the Internet, methods of fraud and ways to obtain important data such as logins and passwords or personal sensitive data have evolved. One way of obtaining such information is to impersonate a page the user knows. Such a site usually does not provide any services other...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2020-09, Vol.178, p.107275, Article 107275
Main Authors: Wei, Wei, Ke, Qiao, Nowak, Jakub, Korytkowski, Marcin, Scherer, Rafał, Woźniak, Marcin
Format: Article
Language:English
Subjects:
Artificial neural networks
Content management systems
Convolutional neural network
Electronic devices
Fraud
Machine learning
Neural networks
Phishing
Urls
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Summary:Along with the development of the Internet, methods of fraud and ways to obtain important data such as logins and passwords or personal sensitive data have evolved. One way of obtaining such information is to impersonate a page the user knows. Such a site usually does not provide any services other than collecting sensitive information from the user. In this paper, we present a way to detect such malicious URL addresses with almost 100% accuracy using convolutional neural networks. Contrary to the previous works, where URL or traffic statistics or web content are analysed, we analyse only the URL text. Thus, the method is faster and detects zero-day attacks. The network we present is appropriately optimised so that it can be used even on mobile devices without significantly affecting its performance.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2020.107275