Webpage feature tampering detection method and device

Abstract

The invention provides a webpage feature tampering detection method and device, electronic equipment and a storage medium, and the method comprises the steps of obtaining webpage sample data, and building a webpage sample data set document; extracting text features, structural features and network features from the data set document; and mining a nonlinear relationship among the text feature, thestructure feature and the network feature, and further detecting the tampering behavior category of the webpage feature. The method is advantaged in that through a heterogeneous feature classificationalgorithm based on deep learning, text features, structural features and network features are effectively fused, different types of webpage tampering behaviors are recognized more accurately, and thefalse alarm rate of detection is reduced.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a method, device, electronic equipment and storage medium for tampering detection of web page features. Background technique [0002] At present, in the field of network security, webpage tampering detection technologies are mainly divided into two types: local detection technology and remote detection technology. Local detection technology refers to deploying the detection program on the website server. At present, there are mainly the following methods: [0003] Use tools to compare the current data of the webpage with the historical data, and propose a campus webpage anti-tampering monitoring system based on content comparison, and display the changed webpages to the administrator for viewing; [0004] Called from the bottom layer of the operating system, the event-based file anti-tampering mechanism can effectively prevent webpage files from being illegally tampered...

AI Technical Summary

Problems solved by technology

[0013] These research methods have carried out useful explorations in webpage tampering, but there are still several main problems: (1) The training set used is too small, the types of webpage tampering considered are simple, and it is difficult to face complex and changeable tampering behaviors

(2) The method used is simple and cannot fully mine the complex nonlinear relationship in different features, which limits the representation ability of the model

(3) Since the research objects of the latter two methods do not directly match, these methods and the data sets used cannot be directly transplanted to the work of web page tampering recognition

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