Training method, a detection method and a system for a malicious webpage detection model

Abstract

The embodiment of the invention provides a training method, a detection method and a system for a malicious webpage detection model. The training method comprises the following steps of: obtaining a data set of a webpage and judgment result data of whether the webpage is a malicious webpage or not; Processing the data set; Establishing a training model architecture; And taking the processing result of the webpage data set as input data, and taking the judgment result as output data to train the training model architecture, so as to form a detection model capable of predicting whether the webpage is a malicious webpage based on the input webpage data processing result. According to the training method in the embodiment of the invention, the detection model for simply and efficiently predicting whether the corresponding webpage is the malicious webpage based on the input network data can be trained, so that the precision is higher when the webpage is predicted through the detection model, and meanwhile, the prediction operation of a user on the webpage is greatly simplified.

Description

technical field [0001] The embodiments of the present invention relate to the field of web page detection, and in particular to a malicious web page detection model training method, a malicious web page detection method and a system. Background technique [0002] With the development of Internet technology, more and more users obtain information through the Internet, creating new opportunities for collecting personal privacy information and organizing criminal activities. Such as all kinds of fraudulent and phishing websites, webpages containing malicious code, pornographic and gambling websites, etc. According to statistics from Kaspersky, malicious webpages appear and play a role in 87.36% of cyber attacks. According to Google's data, about 1.3% of Google's search results are linked to Trojan pages or phishing pages. It can be seen that malicious webpage attacks have surpassed traditional malicious attack forms and become the biggest challenge facing the current network ...

AI Technical Summary

Problems solved by technology

[0007] The disadvantage of static detection technology is that in the face of a large number of new samples to be tested every day, simple feature distribution statistics and heuristic rules can no longer meet the needs. First, the false detection rate increases because of the limitations of heuristic rules and fuzzy matching technology. There are many normal samples that are misjudged as malicious samples because some code fragments match certain templates; secondly, the rule update of this scheme is difficult, and the extraction of rules depends on a lot of professional knowledge and manual summarization, so the rule update higher cost

Images