Abstract: The present disclosure discloses a complex network attack detection method based on cross-host abnormal behavior recognition, and the method includes the following steps: 1) firstly collecting kernel log data of a host to construct an intra-host association graph, then employing a graph convolution network (GCN) to extract features, and finally using an autoencoder to detect abnormal nodes in the intra-host association graph; 2) firstly constructing an inter-host interaction graph by using network flow log data, then training a prediction model based on a sequence of the inter-host interaction graph, and finally detecting abnormal edges in the inter-host interaction graph according to prediction differences; and 3) firstly constructing a cross-host exception association graph according to intra-host and inter-host exception detection results, then spreading exception scores by employing a PageRank algorithm, and finally confirming an attacked host based on the exception scores.

Abstract: A method for ranking a business object is provided. The method includes: obtaining a historical behavior record; extracting at least one of discrete feature information or continuous feature information of at least one business object from the historical behavior record; inputting at least one of discrete feature information or continuous feature information of each business object into a prediction model obtained by pre-training, and predicting a ranking score of each business object; and ranking each business object according to the ranking score of each business object.

Abstract: This application provides a method and an apparatus for determining a similarity between a user and a merchant and an electronic device. According to an embodiment, the method includes: determining first amount distribution of a user based on first historical orders of the user; determining second amount distribution of a merchant based on second historical orders of the merchant; and determining a similarity between the user and the merchant based on the first amount distribution and the second amount distribution.