A method and system for identifying browser uniqueness and risk characteristics

Abstract

The invention discloses a method for identifying the uniqueness and risk features of a browser, comprising the following steps: browser feature data of a client is collected, and browser risk features are detected to get detection data; the browser feature data and the detection data are sent to a server; the server generates an ID uniquely corresponding to the browser according to the received data, and sends the ID back to the browser; and the ID is made persistent. In addition, the invention discloses a system for implementing the method. With the method and the system, the uniqueness of a browser can be identified, the accuracy and persistence of the uniqueness can be ensured effectively, and risk features can be identified. Comprehensive statistics is made on the identity and behaviors of visitors on the basis, and malicious registration, library hitting, activity click farming, vulnerability scanning and other behaviors endangering the Internet product safety can be identified and prevented effectively.

Description

technical field [0001] The invention relates to a method and system for identifying browser uniqueness, in particular to a method and system for identifying browser uniqueness and risk characteristics. Background technique [0002] In today's Internet environment, malicious registration, credential stuffing, activity swiping, and vulnerability scanning are becoming more and more serious, and Internet products are facing great information security challenges. Being able to accurately identify malicious users or robot crawlers from a large number of normal users is the key to identifying such security risks. Identifying the uniqueness of browsers, comprehensively counting user identities and the relationship between user behavior and browsers are common ways to identify the above risks. [0003] The traditional browser unique identification method is to collect the browser data on the user's computer through JavaScript code, and generate the browser characteristic HASH value ...

AI Technical Summary

Problems solved by technology

[0006] (3) The browser feature HASH value of this method has a high repetition rate, which does not correspond to the only browser, and it is easy to cause false positives during risk analysis

[0007] (4) The browser feature information provided by this method is limited, and it can only provide less dimensional data for risk analysis

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