Abnormal access detection method and device

Abstract

The present application discloses an abnormal access detection method, by extracting the time-series data features corresponding to each sample access request, obtaining the value of the corresponding label, and then generating detection according to the value of the label and attribute data corresponding to each sample access request parameter, so after obtaining the attribute data of the access request to be detected, an abnormal probability corresponding to the access request is generated according to the attribute data and detection parameters, and after judging whether the abnormal probability is greater than the preset abnormal threshold, the size of the two can be Check whether the access request is an abnormal access request. Therefore, it is possible to accurately identify and process abnormal access requests among a large number of access requests, ensuring the stability and security of the network.

Description

technical field [0001] The present application relates to the technical field of the Internet, in particular to a method for detecting abnormal access. The present application also relates to an abnormal access detection device. Background technique [0002] Data mining is the process of extracting potential, implicit, and valuable knowledge, patterns, or rules from large-scale data sets. Patterns mined from large-scale data sets can generally be divided into five categories: association rules, classification and prediction, clustering, evolution analysis, and outlier detection. The mining of outlier data includes two parts: outlier data detection and outlier data analysis. Outliers are data that are inconsistent with the general behavior or model of the data. They are outliers in the data set that are not random deviations but arise from entirely different mechanisms. Outlier data mining has a wide range of applications, such as fraud detection, using outlier detection t...

AI Technical Summary

Problems solved by technology

[0012] However, the inventor found in the process of implementing this application that the existing outlier detection algorithm with time series data either only utilizes the characteristic data of the visiting user itself for clustering, and can only reflect the characteristics of the visiting user's attributes; or only Using the time series data of access, manually set the threshold to find some abnormal points (that is, confirm that the current access is abnormal)

These two methods do not give full play to the value of the data, and the results are often not very accurate and effective

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