Abnormal event monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to the field of exception monitoring, and provides an abnormal event monitoring method and device, computer equipment and a storage medium. The method comprises the following steps: receiving event data; performing association event analysis and classification analysis on the event data through a CEP engine model to obtain a plurality of basic index values; performing event classification on the event data through an event management model to obtain event types, and performing event analysis and event analysis on the event data through the event management model to obtaina plurality of potential index values; obtaining an event prediction model and an abnormal threshold matched with the event type; performing combination and abnormal feature extraction on all the basic index values and all the potential index values through an event estimation model to obtain an estimation result; and determining a monitoring result of the event data according to the estimation result and the abnormal threshold. According to the invention, whether the event data is the abnormal event or not can be accurately, comprehensively and automatically identified, and the security of the event data is ensured.

Description

Technical field [0001] The present invention relates to the field of abnormal monitoring, in particular to an abnormal event monitoring method, device, computer equipment and storage medium. Background technique [0002] At present, the development of e-commerce platforms has brought great convenience to people’s lives. However, because criminals attempt to obtain greater benefits in the process of e-commerce transactions, various types of transactions have appeared in the process of e-commerce transactions. Abnormal behaviors, such as the theft of account transactions, malicious cashing out, and malicious swindling of wool. In order to reduce the possibility of abnormal abnormal behaviors, e-commerce platforms will detect the user’s transaction process in order to execute the abnormal behavior Monitor abnormality. However, in the prior art, most of the solutions can only prevent abnormal behaviors and reduce the impact of abnormal behaviors by passively setting the type and thr...

AI Technical Summary

Problems solved by technology

However, in the existing technologies, most of the schemes can only prevent abnormal behaviors and reduce the impact of abnormal behaviors by passively and artificially setting the type and threshold of transaction amount, and the definition conditions for judging whether it is abnormal behaviors are single, and the definition conditions The quota threshold is also inaccurate, so it is difficult to effectively identify abnormal behaviors, which leads to the existence of a high false blocking rate for abnormal behaviors, which has a great impact on users' legitimate operations

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