Threshold-self-adaptive-correcting network flow monitoring system and method

Abstract

The invention provides a threshold-self-adaptive-correcting network flow monitoring system and method. The system includes a flow acquisition module, a self-adaptive threshold correction module, a flow monitoring module and an alarm display module. The flow acquisition module obtains flow information data from a switch and transmits the flow information data to the self-adaptive threshold correction module and the flow monitoring module. The self-adaptive threshold correction module confirms a threshold parameter initial value and updates the threshold parameter and transmits the updated threshold parameter to the flow monitoring module. The flow monitoring module compares current flow and historical flow conditions and judges whether accumulated-flow mutation happens in a network and uses the current flow as historical flow which is then saved as flow historical data after the judgment is completed and transmits the judgment result to the alarm display module. The alarm display module performs displaying through a man-machine interaction interface according to a calculation result transmitted by a flow detection module.

Description

technical field [0001] The invention belongs to the technical field of computer communication, and in particular relates to a network flow monitoring system and method for self-adaptive correction of a threshold. Background technique [0002] With the development of network technology, the bandwidth of the network is constantly increasing, and the transmission rate is getting higher and higher. Generally speaking, due to the existence of the load balancing mechanism in the network protocol, the load of each node in the network is generally is relatively balanced. Traffic mutations generally occur when the network fails, so mutation detection plays an important role in ensuring network security and reliability. [0003] At present, there have been a lot of related researches on the algorithm of data stream mutation detection with adaptive features. Generally speaking, in order to meet the requirements of adaptive traffic conflict detection, the conflict detection is mainly ...

AI Technical Summary

Problems solved by technology

[0004] As we all know, the abnormal threshold selection of traditional technical solutions is a fixed value, or an empirical value. For data transmission systems in aerospace testing and other fields, the network environment changes greatly, and it is impossible to select the threshold value based on empirical values ​​every time. Therefore, the above The technology cannot be applied to situations where dynamic network traffic changes in real time

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