by Wei Wei, Qiao Ke, Jakub Nowak, Marcin Korytkowski, Rafał Scherer, Marcin Woźniak
Abstract:
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.
Reference:
Accurate and Fast URL Phishing Detector: A Convolutional Neural Network Approach (Wei Wei, Qiao Ke, Jakub Nowak, Marcin Korytkowski, Rafał Scherer, Marcin Woźniak), In Computer Networks, 2020.
Bibtex Entry:
@article{WEI2020107275,
title = "Accurate and Fast URL Phishing Detector: A Convolutional Neural Network Approach",
journal = "Computer Networks",
pages = "107275",
year = "2020",
issn = "1389-1286",
doi = "https://doi.org/10.1016/j.comnet.2020.107275",
url = "http://www.sciencedirect.com/science/article/pii/S1389128620301109",
author = "Wei Wei and Qiao Ke and Jakub Nowak and Marcin Korytkowski and Rafał Scherer and Marcin Woźniak",
keywords = "Phishing, Urls, Machine learning, Convolutional neural network",
abstract = "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."
}