Social network topology model and construction method, user confidence and intimacy calculation method and telecommunication fraud intelligent interception system

Abstract

The invention discloses a social network topology model, and the model is a three-layer two-order star-shaped structure constructed by taking each target user as a center and comprises a first-layer node, second-layer nodes and third-layer nodes; the first-layer node is a target user, the second-layer nodes are a direct called user, and the third-layer node is obtained by modularization of neighbor nodes generated by each second-layer node; the node value of the social network topology model is the user confidence, and the edge weight of the social network topology model is the intimacy between users. The invention further discloses a construction method of the social network topology model, a user confidence coefficient and intimacy calculation method and a telecommunication fraud intelligent interception system. The social network topology model does not infinitely increase the data volume over time. According to the telecommunication fraud intelligent interception system, based on user historical data, the infinite increase of the system data size along with time can be avoided, and original and newly-added fraud users can be accurately identified.

Description

technical field [0001] The invention relates to the field of telecommunications, in particular to a social network topology model based on telecommunications call records. The present invention also relates to a construction method of the social network topology model, a user confidence iterative update subsystem utilizing the social network topology model, and a telecommunications fraud system with the user confidence iterative update subsystem Intelligent interception system. Background technique [0002] With the continuous development of society, the updating of science and technology and the wide application of Internet technology, telecommunications fraud has become one of the most common means of fraud, which has attracted widespread attention from all sectors of society. At present, various operators are actively studying various methods to deal with the problem of telecom fraud, so as to reduce economic losses. At present, a telecommunications fraud identification...

AI Technical Summary

Problems solved by technology

The advantage of this technology is that it can flexibly detect fraudulent users based on user behavior. The disadvantage is that it does not take into account the long-term social relationship between users and the trust degree of user historical behavior. When the number of ordinary users far exceeds that of fraudulent users, The performance of the model will be greatly limited

[0005] Chinese patent (CN201710721594.9) discloses a telecom fraud recognition and defense system based on deep learning. Its technical defect is that the existing telecom fraud recognition system uses speech recognition technology to detect abnormal information in the call content, which is This method requires the mobile phone to install the specified App, which will record all the call content of the user and convert it into text and upload it to the server

In terms of fraud identification technology, only the text content information in the current call is considered, and the historical behavior of the calling number, the intimacy relationship with the called number, etc. are not considered comprehensively, so it may cause misjudgment

[0006] Chinese patent (CN201910110374.1) discloses a communication fraud identification method and system, and its technical defects are: the existing fraudulent user identification technology is based on user call frequency, terminal location and whether the called party belongs to a group prone to fraud , to identify whether the calling party is communication fraud

This identification technology has relatively large limitations. In terms of judging the frequency of calls, it only uses a predetermined threshold for screening, and cannot reasonably distinguish fraudulent users, harassing users, and ordinary users with a large number of calls.

In terms of terminal location, it is susceptible to the influenc

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