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Anomaly detection method for industrial control network signals based on depth learning structure

An industrial control network and deep learning technology, applied in the field of outlier detection in industrial control network data, can solve the problem of difficult detection of a small number of outliers, and achieve the effect of improving the ability

Active Publication Date: 2018-12-18
HARBIN INST OF TECH
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  • Application Information

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Problems solved by technology

[0011] The purpose of the present invention is to solve the problem that in the existing method, it is difficult to detect a small number of abnormal values ​​and it is necessary to artificially define the difference between normal data and abnormal values.

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  • Anomaly detection method for industrial control network signals based on depth learning structure
  • Anomaly detection method for industrial control network signals based on depth learning structure
  • Anomaly detection method for industrial control network signals based on depth learning structure

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specific Embodiment approach 1

[0024] Specific implementation mode one: refer to Figure 1 to Figure 4 Specifically illustrate this embodiment, the industrial control network signal anomaly detection method based on the deep learning structure described in this embodiment, the method includes the following steps:

[0025] Step 1. Select part of the data from the industrial control network data to mark as a training sample, perform data normalization and standardization operation on the training sample, obtain the normalized calibrated data, and use the data enhancement algorithm to normalize the normalized calibrated data. The calibration data adds some false sample values ​​to form the detected data;

[0026] Step 2, input the normal data and the detected data into an autoencoder compression network for training, respectively obtain the spliced ​​data of the normal data and the spliced ​​data of the detected data;

[0027] Step 3, input the two spliced ​​data into the comparison network and calculate the ...

specific Embodiment approach 2

[0044] Specific embodiment 2: This embodiment is a further description of the industrial control network signal anomaly detection algorithm based on the deep learning structure described in the specific embodiment 1. In this embodiment, in step 1, data normalization is performed on the training samples The formula for the normalization operation is:

[0045]

[0046] In the formula, is the mathematical expectation of the training sample data, σ x is the standard deviation of the training sample data, x k is the normalized calibrated data, x m is the calibrated data before normalization;

[0047] In step 1, the formula for adding some fake samples to the normalized calibrated data using the data enhancement algorithm is:

[0048]

[0049]

[0050] In the formula, λ is a value in the range of (0,1), (x i ,y i ),(x j ,y j ) are two samples randomly obtained from the normalized calibrated data; is the value and label of the generated "fake samples".

specific Embodiment approach 3

[0051] Specific embodiment three: This embodiment is a further description of the industrial control network signal anomaly detection algorithm based on the deep learning structure described in specific embodiment one. In this embodiment, in step three, the two spliced ​​data Input to the comparison network and calculate the distance between the normal data and the detected data through the calculation of the deep neural network is as follows:

[0052] Using the parameter θ c neural network to learn the difference L(f n ,f a ; θ c ), the distance f(x) between the normal data and the detected data is obtained as:

[0053] f(x)=L([x n ,z n ],[x a ,z a ]; θ c ) Formula 3,

[0054] where f n represented by normal data X n and the compressed value Z for normal data n Splicing data of normal data obtained by splicing and combining, f a Indicates that by the new detected data X a and the detected data compression value Z a The spliced ​​data of the detected data obtain...

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Abstract

An anomaly detection method of industrial control network signal based on depth learning structure relates to the technical field of outlier detection in industrial control network data. The present invention aims at solving the problem that it is difficult to detect a small amount of abnormal values because the normal data and the abnormal values need to be defined artificially in the existing method. The normalized calibrated data are obtained by data normalization and the normalized calibrated data are formed by adding some false sample values to the normalized calibrated data with the dataenhancement algorithm. The normal data and the detected data are input into a self-encoder compression network for training, and the trained data are obtained respectively; the distance between the normal data and the detected data is calculated by the depth neural network, and the classifier is used to judge the abnormal value of the detected data according to the distance. It is used for signalanomaly detection.

Description

technical field [0001] The invention relates to an industrial control network signal abnormality detection method based on a deep learning structure, and belongs to the technical field of abnormal value detection in industrial control network data. Background technique [0002] Industrial Control System (Industrial Control System, ICS) refers to an automatic control system composed of computers and industrial process control components, which consists of controllers, sensors, transmitters, actuators, and input / output interfaces. These components are connected through industrial communication lines according to a certain communication protocol to form an industrial manufacturing or processing system with automatic control capabilities. [0003] The current industrial control system usually involves the following types of networks during specific deployment: enterprise office network (enterprise network or office network), process control and monitoring network (monitoring net...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06F2218/12G06F18/214
Inventor 曲海成秦济韬陈浩
Owner HARBIN INST OF TECH
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