Network information risk identification method and system

Abstract

The invention discloses a network information risk identification method and a network information risk identification system. The method comprises the steps of obtaining network information data which comprises structured data and unstructured data; normalization processing is conducted on the network information data, normalization results are stored in a corpus feature library, and the normalization results comprise normalization results corresponding to the structured data and entry vector sequences corresponding to the unstructured data; inputting the entry vector sequence corresponding to the unstructured data into a pre-trained document vector sequence generation model, and outputting the document vector sequence corresponding to the unstructured data; and inputting the normalization result corresponding to the structured data and the document vector sequence corresponding to the unstructured data into a pre-trained risk prediction model, and outputting a risk prediction resultof the network information data, the risk prediction result being further used for updating the corpus feature library. The effect of quickly and accurately identifying the enterprise risk is achieved.

Description

technical field [0001] The present invention relates to the field of Internet, and in particular, to a method and system for identifying risks of network information. Background technique [0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] With the rapid development of Internet technology, especially the advent of the era of big data, there is more and more information about enterprises on the Internet, and most of this information is unstructured and has not been effectively utilized. Mining relevant information about enterprise risks from unstructured information and transforming it into structured data to intuitively represent enterprise risk status is very important for timely understanding of enterprise network information risk status and improving the ability of enterprises to prevent risks. signif...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention provides a network information risk identification method, which is used to solve the technical problem that the existing network information risk identification method is based on the feature database maintained by experts or based on the artificial speech rule library. Due to the low update efficiency, the latest risk information cannot be identified. The method includes: acquiring network information data, wherein the network information data includes: structured data and unstructured data; performing normalization processing on the network information data, and storing the normalized result in a corpus feature database, wherein the normalization The normalized result includes the normalized result corresponding to the structured data and the entry vector sequence corresponding to the unstructured data; the entry vector sequence corresponding to the unstructured data is input into the document vector sequence generation model obtained in advance, and the output is not The document vector sequence corresponding to the structured data; the normalized result corresponding to the structured data and the document vector sequence corresponding to the unstructured data are input into the risk prediction model obtained by pre-training, and the risk prediction result of the network information data is output, where , the risk prediction result is also used to update the corpus feature library

[0006] The embodiment of the present invention also provides a network information risk identification system, which is used to solve the technical problem that the existing network information risk identification method is based on the feature database maintained by experts or based on the artificial speech rule library. Due to the low update efficiency, the latest risk information cannot be identified. , the system includes: a data collection and processing unit for collecting network information data, wherein the network information data includes: structured data and unstructured data; a data normalization processing unit connected with the data collection and processing unit for processing The network information data is normalized, and the normalized result is stored in the corpus feature database, wherein the normalized result includes the normalized result corresponding to the structured data and the entry vector sequence corresponding to the unstructured data; the document The vector sequence generation unit is connected with the corpus feature library, and is used to process the entry vector sequence corresponding to the unstructured data based on the pre-trained document vector sequence generation model, and generate the document vector sequence corresponding to the unstructured data; risk The model training unit is connected to the document vector sequence generation unit and the corpus feature library respectively, and is used to receive the document vector sequence output by the document vector sequence generation unit, and to find the normalized results corresponding to the structured data from the corpus feature library, and based on The pre-trained risk prediction model processes the normalized results corresponding to the structured data and the document vector sequences corresponding to the unstructured data to obtain the risk prediction results of the network information data, wherein the risk prediction results are also used to update all corpus feature library

[0007] The embodiment of the present invention also provides a computer device, which is used to solve the technical problem that the latest risk information cannot be identified due to the low update efficiency of the existing network information risk identification method based on the feature library maintained by experts or based on the artificial speech rule library. The device includes a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, the above-mentioned network information risk identification method is realized.

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