Group Harmful Call Detection Method, Device and Readable Medium Based on Flow Data Graph

Abstract

The present invention provides a group harmful call detection method, device and readable medium based on stream data graph, comprising: collecting CDR data of multiple users, decomposing the CDR data to obtain communication data and storing it in a graphic database, and obtaining a communication graph; Based on the depth map of the sliding window, the statistics of communication nodes and the relevant attributes and representations of subgraphs are extracted from the communication graph; The contact closeness of the call piece confirms the corresponding harmful call group. The beneficial effect of the present invention is that the real-time performance and accuracy are improved to a large extent, and it is possible to view the call current and combined with the history of the map features and record traceability in real time, repeat the way of listening to the call for confirmation, and increase the analysis of group calls. Success rate.

Description

technical field [0001] The invention belongs to network science and telecommunication network data analysis, and in particular relates to a group harmful call detection method, device and readable medium based on stream data graph. Background technique [0002] With the popularity of wireless communication technology, mobile phones have become an essential way for people to make long-distance calls, bringing great convenience to life. However, everything has two sides. While wireless communication technology provides convenience for life, it is also easy to be used by criminals to commit crimes. Telecom fraud is a new type of fraud derived from the popularization of mobile communications. Fraud methods have caused huge losses to life and economy, and how to effectively detect harmful calls is imminent. [0003] At present, the widely used harmful call detection technology mostly discovers the potential trend of telecom fraud by analyzing the calling user's behavior. This ...

AI Technical Summary

Problems solved by technology

However, the above-mentioned methods all rely on post-processing analysis. Although they have a certain effect on the prevention of telecom fraud, they cannot achieve the expected effect.

In addition, the existing methods are not very accurate in detecting fraudulent gangs, and cannot timely and accurately analyze the location of fraudulent gangs and their members from massive data

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