Research And Implementation Of Telecommunication Anti-Fraud Platform Based On Graph Neural Network

With the development of Internet big data and the communications industry,the technical threshold for telecoms fraud is also being lowered,making telecoms fraud increasingly rampant in recent years,with fraudulent tactics becoming more and more specialized and covert.The current telecommunication anti-fraud means mainly through the black and white list database and based on the call signaling characteristics and other methods,there is a certain degree of passivity,cannot effectively solve the current increasingly complex fraud problems.Therefore,based on the problem of fraud identification in different scenarios of telecommunication fraud,this paper proposes a fraudulent user identification algorithm based on pairwise hypergraph and a fraudulent user identification algorithm based on dynamic communication network respectively,in order to cope with the changing fraud tactics.Furthermore,in order to provide a supporting platform for the proposed fraudulent user identification algorithm,this paper designs and implements a telecom anti-fraud platform based on graph neural network.Staff can upload call signaling data,use the algorithm provided by the platform to detect and analyze telecommunications fraud,and help relevant anti-fraud personnel make decisions.Based on the background of telecommunications anti-fraud,this topic proposes a target user communication subgraph sampling algorithm based on relative relationship strength and a fraudulent user identification algorithm based on dual hypergraph.The former models the relationship strength between users in the communication network and the communication subgraph of the target user is sampled,which reduces the noise in the network and reduces the network scale,which is convenient for the training of subsequent models;on the basis of the former algorithm,the latter introduces dual hypergraph conversion,which can mine the information contained in the communication behavior.The high-dimensional features of the network,and learn the network topology and user feature information in the communication sub-graph through the graph neural network,which improves the accuracy of fraudulent user identification.At the same time,in order to identify fraudsters with strong concealment and increasingly complex fraud methods in the ever-changing communication network,this paper proposes a fraudulent user identification algorithm based on a dynamic communication network.The network evolution graph is a convolutional network,and the importance of users in different time slices is extracted through a temporal attention mechanism to identify fraudulent users in a dynamic network.The paper conducts comparative experiments on the proposed method based on real and public data sets,and deploys and tests the designed and implemented telecom anti-fraud platform.The experimental results prove the effectiveness of the fraudulent user identification method proposed in this paper.