Website content safety testing system and method

Abstract

The invention discloses a website content safety testing system and method. The system includes a front-end request module, a web spider module, a feature extraction module, a model training device, an FPGA hardware accelerator and a safety arbitration module; the front-end request module inputs to-be-tested URLs and submits a request to the web spider module; the web spider module crawls pictureinformation of a target URL; the feature extraction module extracts the picture information of the web spider module and the picture information of a sample picture module as feature vectors; the model training device utilizes the feature vectors of sample pictures to generate a classification device by means of a supervised learning mode; the FPGC hardware accelerator provides a hardware acceleration function for the feature extraction module; the safety arbitration module calculates the safety coefficient of the target URL according to a classification result obtained after the classification device classifies the features of the pictures. By means of the principle above, by inputting the features of the sample pictures into the model training device to obtain the classification device and using the FPGA hardware accelerator to accelerate an algorithm of the feature extraction module to increase the response speed of the system, the purpose of implementing a fast, efficient and accurate website content safety test is achieved.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a website content security detection system and method. Background technique [0002] With the development of Internet technology, Web applications have brought great convenience to people's life and greatly enriched the way of dissemination of information. However, some illegal elements seek benefits for themselves by creating websites such as phishing, gambling and pornography, which brings great security risks to people's safety and health when surfing the Internet. Therefore, the detection of malicious websites has become a serious network security problem. [0003] At present, the detection of malicious web pages mainly includes two methods: static feature detection and dynamic feature detection. Static feature detection includes analysis of web page DNS information, WHOIS information, URL syntax features, HTML content, and JavaScript code; dynamic feature detecti...

AI Technical Summary

Problems solved by technology

This method has the following disadvantages: (1) Some malicious URLs have no obvious malicious features in grammatical features and WHOIS registration information, which are very similar to normal URLs, and the false positive rate is high; Content analysis, only by analyzing DNS, WHOIS and URL information to judge the security of the URL is one-sided

The above two methods have greatly improved compared with Justin's research, but they have ignored several important issues: (1) For the classification of web content, especially for the classification of pictures, use SVM model or BP neural network classification The performance of complex images is not good, and it is easy to produce large deviations; (2) Using machine learning or deep learning to classify web content will bring great overhead to the system. Measures of system response speed, the two did not do similar acceleration processing

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