Traffic identification method and device, electronic device and storage medium

Abstract

The invention discloses a traffic identification method and device, an electronic device and a storage medium. The method comprises the following steps of monitoring the flow of a target service, andcalculating the flow mean value ratio of the target service; if the calculated traffic mean value ratio exceeds a first threshold value, determining a traffic sudden increase moment; obtaining the user data corresponding to the traffic sudden increase moment, and screening out abnormal user data in the user data based on a graph semi-supervised method; and determining an abnormal user according tothe abnormal user data, and identifying the flow of the abnormal user as abnormal flow. According to the technical scheme, a mean shift thought is adopted to the monitor traffic sudden increase, theabnormal traffic generation is determined, a graph semi-supervised learning algorithm can represent the similarity of access behaviors which are not intercepted, the interpretability is high, the requirement for data is lower than that of an unsupervised algorithm, and an obtained result is more stable; and the omission is avoided through a mode of firstly determining the abnormal users and then identifying the abnormal flow.

Description

technical field [0001] The present application relates to the technical field of traffic identification, and specifically relates to a traffic identification method, device, electronic equipment and storage medium. Background technique [0002] Crawlers are a common means of crawling resources from the network. Although a large amount of resources can be obtained, for network service providers, there will be traffic impacts, and even service paralysis, resulting in poor experience for customers. Therefore, many network service providers are interested in identifying abnormal traffic caused by crawlers, filtering the traffic, and maintaining the stability of network services. [0003] At present, building a crawler identification model based on big data and machine learning has achieved some preliminary results in the prediction, detection and identification of abnormal traffic, but there are shortcomings such as unstable results and narrow applicable scenarios. Traffic ident...

AI Technical Summary

Problems solved by technology

[0003] At present, building a crawler identification model based on big data and machine learning has achieved some preliminary results in the prediction, detection and identification of abnormal traffic, but there are shortcomings such as unstable results and narrow applicable scenarios. Traffic identification technology still has a lot of room for improvement.

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