Abnormal behavior detection method and device, equipment and storage medium

Abstract

The invention relates to an abnormal behavior detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining a process operation behavior sequence, dividingthe process operation behavior sequence into a plurality of to-be-detected sequences, enabling each to-be-detected sequence to comprise a plurality of user process operation behaviors, and enabling each user process operation behavior to comprise a plurality of pieces of field information; performing feature extraction based on the attribute values of the plurality of pieces of field informationof each process operation behavior in the to-be-detected sequence to obtain a feature vector corresponding to the to-be-detected sequence; performing abnormal detection on the feature vectors corresponding to the to-be-detected sequences, and determining the feature vectors detected to be abnormal to be abnormal feature vectors; determining an anomaly degree of each dimension feature in the anomaly feature vector; determining an abnormal feature item in the abnormal feature vector based on the abnormality of each dimension feature in the abnormal feature vector; according to the method, the accuracy and efficiency of user abnormal behavior detection can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to an abnormal behavior detection method, device, equipment and storage medium. Background technique [0002] With the continuous development of computer technology, it has become convenient and fast to provide users with services through computer technology, but at the same time, it will also be used by some illegal industries. These illegal industries will have a certain degree of adverse effects on normal users and computer technology service platforms. ; In order to reduce the impact of illegal industries on normal users and service platforms, it is necessary to analyze user behavior data, and monitor user activities based on the analysis results, so as to detect abnormal users in time. [0003] In the prior art, when detecting user behaviors, a single user behavior is generally matched with preset matching rules. The preset matching rules are simple, obtained ba...

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Problems solved by technology

[0003] In the prior art, when detecting user behaviors, a single user behavior is generally matched with preset matching rules. The preset matching rules are simple, obtained based on limited experience, and are generally strong rules, which are difficult to detect. Matching detection based on weak rules leads to low accuracy and efficiency of user abnormal behavior detection, so an effective abnormal behavior detection method is needed to realize user behavior detection

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