A group abnormal behavior detection method based on deep structure learning
A detection method and anomaly technology, applied in the field of computer information, can solve problems such as difficult acquisition, no effect of multi-abnormal groups, and easy tampering of attribute information by fraudsters, etc., to achieve good results
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[0035] In order to make the above-mentioned features and advantages of the present invention more comprehensible, the following specific embodiments are described in detail in conjunction with the accompanying drawings.
[0036] The invention utilizes a deep neural network to complete the embedding of a bipartite graph network, and in combination with a density-based clustering method, proposes a group abnormal behavior detection method based on deep structure learning.
[0037] The realistic assumption that the present invention is based on is: fraudsters will comment on target products as much as possible, while ordinary users will not comment too much on these products, and the present invention hopes to detect All anomalous groups that contain groups of fraudsters and corresponding target items.
[0038] Such as image 3 As shown, the specific solution idea of the present invention is: for a given bipartite graph G=(U, V, E), firstly, the source nodes and sink nodes in ...
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