Traffic prediction method based on neural network

A traffic prediction and neural network technology, applied in the field of traffic prediction based on neural network, can solve problems such as collecting historical data, and achieve the effects of wide application range, fast detection speed, and high degree of automation

Inactive Publication Date: 2018-06-29
宝牧科技(天津)有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing technical solutions generally use the ARMA model to predict Internet traffic, but have the following disadvantages: First, it is necessary to manually collect a large amount of historical data
Second, the obtained prediction function is only applicable to the network cable environment where the data was collected at that time

Method used

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  • Traffic prediction method based on neural network

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Embodiment Construction

[0020] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0021] In describing the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", " The orientations or positional relationships indicated by "vertical", "horizontal", "top", "bottom", "inner" and "outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and Simplified descriptions, rather than indicating or implying that the device or element referred to must have a particular orientation, be constructed and operate in a particular orientation, and thus should not be construed as limiting the invention. In addition, the terms "first", "second", etc. are used for descriptive purposes only, and should not be understood ...

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Abstract

The invention provides a traffic prediction method based on a neural network. The method comprises the following steps: sampling computer data according to a set sampling time period, determining a window length of a training set as 3, and realizing the prevention and detection of abnormal traffic by cooperatively using the data sampling, data set setting, LSTM model training and data judgment. The method provided by the invention has the features of being high in automation degree, fast in detection speed and wide in application range.

Description

technical field [0001] The invention belongs to the field of network artificial intelligence, and in particular relates to a flow prediction method based on a neural network. Background technique [0002] With the development of information technology, industrial control systems are gradually becoming open, interconnected and universal. Many industrial control protocols are gradually running on industrial Ethernet. If the traffic speed of industrial Ethernet can be predicted, it will provide a strong technical guarantee for abnormal traffic identification of industrial Ethernet. The existing technical solutions generally use the ARMA model to predict Internet traffic, but have the following disadvantages: First, a large amount of historical data must be manually collected. Second, the obtained prediction function is only applicable to the network cable environment where the data was collected at that time. Third, each different scenario requires different separate analysis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06H04L12/24
CPCH04L41/142H04L41/147H04L63/1425H04L63/1458
Inventor 滕建桓
Owner 宝牧科技(天津)有限公司
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