Network service anomaly detection method and device based on attribute network representation learning

Abstract

The invention discloses a network service anomaly detection method and device based on attribute network representation learning, and the method comprises the steps: obtaining initial network servicedata, constructing a heterogeneous information network based on the initial network service data, and obtaining a node attribute set; constructing an attribute vector set based on the node attribute set, and constructing an attribute information network according to the attribute vector set and the heterogeneous information network; constructing a target function based on the attribute informationnetwork, and constructing a mapping relationship between nodes in the attribute information network and vector representations corresponding to the nodes based on vectors corresponding to the nodes to be learned in network representation learning obtained by solving the target function; and training based on the training set data to obtain an anomaly detection model, and calculating an anomaly probability of each piece of network service data in the test set data according to the anomaly detection model. According to the method, the relevance of the nodes in the attribute information networkis enhanced, the generalization ability of the anomaly detection model is improved, and better guarantee is provided for anomaly detection, anomaly interception and fund security protection of users and enterprises.

Description

technical field [0001] The invention relates to the technical field of network service anomaly detection, in particular to a method and device for detecting anomalies in attribute network representation learning in network services. Background technique [0002] At present, with the gradual expansion of network service business, fraudulent methods emerge in an endless stream. In order to protect the business security of financial institutions and normal users in network service, it is necessary to establish an effective network service anomaly detection system. [0003] There are usually potential correlations in abnormal information in network services. Anomaly detection based on knowledge graphs mainly converts "single point" information into a "flat" interrelated network structure, effectively analyzing the specific relationships existing in complex relationships. potential risks. In recent years, network representation learning has shown a powerful role in mining the as...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that the review of anomaly detection methods in traditional network services is slow, it is difficult to capture the correlation between information, and it lacks good generalization ability, and the existing anomaly detection methods based on network representation learning are too isolated. Network service information cannot be processed, and its universality is weak in various scenarios of network services

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