Method and device for identifying phishing websites based on deep learning

A phishing website and deep learning technology, applied in the field of deep learning, can solve the problem of low efficiency of phishing website identification methods, and achieve the effects of short detection time, possibility of avoidance, and strong generalization.

Active Publication Date: 2021-12-28
YUNNAN UNIVERSITY OF FINANCE AND ECONOMICS
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AI Technical Summary

Problems solved by technology

[0006] The main purpose of this application is to provide a method and device for identifying phishing websites based on deep learning to solve the technical problem of low efficiency of phishing website identification methods in the related art

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  • Method and device for identifying phishing websites based on deep learning
  • Method and device for identifying phishing websites based on deep learning
  • Method and device for identifying phishing websites based on deep learning

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Embodiment Construction

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is an embodiment of a part of the application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0035]It should be noted that the terms "first" and "second" in the description and claims of the present application and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It should be understood that the data so used may be interchanged under appropriate circumstances for ...

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Abstract

The present application discloses a method and device for identifying phishing websites based on deep learning. The method includes inputting the website address of the target phishing website as a training website data set into a first preset model; training the first preset model according to the training website data set to extract a first feature set; set and a preset second feature set to obtain a combined feature set; according to the combined feature set, input the training website data set into a second preset model for training, and output a phishing website discrimination model. The present application solves the technical problem that the phishing website identification method in the related art is not efficient. Through this application, the detection time of phishing websites is short, the accuracy of detection results is high, and the discrimination method has stronger generalization in practical applications.

Description

technical field [0001] The present application relates to the technical field of deep learning, in particular, to a method and device for identifying phishing websites based on deep learning. Background technique [0002] The concept of deep learning originated from the research of artificial neural networks, and the multi-layer perceptron with multiple hidden layers is a deep learning structure. Deep learning combines low-level features to form more abstract high-level representation attribute categories or features to discover distributed feature representations of data. [0003] "Phishing website" is a kind of network fraud, which means criminals use various means to counterfeit the URL address and page content of the real website, or use the loopholes in the real website server program to insert dangerous HTML codes into some web pages of the site , In order to defraud the user's bank or credit card account number, password and other private information. [0004] The a...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/06G06N20/00
CPCH04L63/1483G06N20/00
Inventor 冯涛刘蕊
Owner YUNNAN UNIVERSITY OF FINANCE AND ECONOMICS
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