User abnormal transaction account detection method based on mahalanobis distance technology

A user account and abnormal transaction technology, applied in the field of big data processing, can solve the problem that abnormal transaction account detection is difficult to meet the actual use requirements, and achieve the effect of reducing omission, improving performance, and easy to capture
CN114820189APending Publication Date: 2022-07-29安徽兆尹信息科技股份有限公司 +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
安徽兆尹信息科技股份有限公司
Publication Date
2022-07-29

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Abstract

The invention relates to a user abnormal transaction account detection method based on a mahalanobis distance technology. Compared with the prior art, the defect that abnormal transaction account detection is difficult to meet actual use requirements is overcome. The method comprises the following steps: acquiring user account activity data; constructing an interactive network directed graph; an egonet model is initialized; acquiring data of a to-be-detected user account; feature extraction of the egonet model is carried out; and detecting the abnormal transaction account of the user. According to the method, the transaction activity of the user account under the transaction network model is depicted and analyzed to design the method for detecting the abnormal transaction account of the user based on the structured graphic data, so that the performance of a detection algorithm is improved, and the omission of the abnormal activity is reduced.
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Description

technical field

[0001] The invention relates to the technical field of big data processing, in particular to a method for detecting abnormal user transaction accounts based on Mahalanobis distance technology. Background technique

[0002] The detection of abnormal transaction accounts is the core issue of big data security. Traditional detection methods based on statistical analysis and operation sequences cannot comprehensively detect abnormal behaviors in transaction activities of user accounts. At the same time, the detection method based on statistical analysis and operation sequence analyzes independent user operations, and cannot determine the malicious activities of the attacker using multiple accounts at the same time; the AUC of the anomaly detection algorithm based on local density or small group is not high, and it will Missing some anomalous activity.

[0003] However, the analysis based on structured graph data has strong representation ability and robustness a...

Claims

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