Intelligent network-related crime risk revealing method

Abstract

The invention discloses an intelligent network-related crime risk revealing method, which comprises the following steps of: monitoring and classifying text contents of related network activity data, performing extraction analysis and integral operation on the related network activity data by combining a defined basic library and applying a rule engine, big data comparison and a text analysis technology; and outputting the related scores of the series of network activities, endowing and accumulating the points to the related network account numbers, and obtaining the final risk level of the account numbers after risk level conversion. According to the invention, online activity text data such as online speech, communication information, transaction shopping, logistics delivery and the like can be detected, risk indexes of related accounts can be obtained through comprehensive operation, and objective analysis can be rapidly carried out on the target account. Therefore, the workload of manually checking the network account and the activity behavior of the network account one by one by workers is relieved, the manual research and judgment difficulty is reduced, and the workers can quickly predict the risk of the account related to the behavior.

Description

technical field [0001] The present invention relates to the technical field of the public security industry, and more specifically to a method for intelligently revealing cybercrime risks, which is used for pre-detection and discovery of cybercrime activities in the public security industry. Background technique [0002] In today's era of Internet + and big data, people's life behaviors are increasingly networked and data-based, and criminals are gradually transferring traditional criminal activities to the Internet. Opaque and other characteristics have brought greater challenges to the staff's crime prevention and combat activities. [0003] At present, in the daily network activity investigation work, although the staff can obtain online activity data, due to the large amount of data, and there is a large amount of manual work for the classification, analysis and identification of data, there is a lack of scientific and comprehensive analysis of network activity data. Fa...

AI Technical Summary

Problems solved by technology

[0003] At present, in the daily network activity investigation work, although the staff can obtain online activity data, due to the large amount of data, and there is a large amount of manual work for the classification, analysis and identification of data, there is a lack of scientific and comprehensive analysis of network activity data. Fast and effective intelligence analysis methods affect both efficiency and effectiveness

[0004] At present, some large Internet companies on the market have some related software for monitoring and detecting network activities, but they are all based on general sensitive word matching technology to do some general comparison processing, and the comparison results are directly pushed to relevant websites as clues. units, the clues are not sorted out, and it is not transformed into an account-centric and intuitive and easy-to-understand risk index from the perspective of risk management and control; and there is no targeted denoising processing and sensitive word replacement mechanism for combining related businesses, resulting in The clues pushed are inaccurate, the quantity is large, and the workload of manual intervention is large; at the same time, the risk index of the account related to the network behavior is not revealed from the perspective of risk control and points, and the staff cannot be intuitively told which type of network they should pay more attention to. Activities; Lack of custom configurable rule engine technology, unable to quickly and cost-effectively respond to changes in subsequent user needs

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