Method for detecting power grid false data injection attacks

A technology of injecting attacks and fake data, applied in electrical components, biological neural network models, instruments, etc., can solve problems such as threats to the stable operation of power grids, power outage losses, and impact on smart grids, achieving simple construction, fast convergence, and improved detection efficiency. Effect

Inactive Publication Date: 2020-04-17
STATE GRID ZHEJIANG ELECTRIC POWER CO LTD HANGZHOU POWER SUPPLY CO +1
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[0003] Malicious network attacks, such as false data injection attacks (false data injection attack, FDIA), etc., can tamper with the measurement data in SCADA, thereby affecting the results of

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  • Method for detecting power grid false data injection attacks
  • Method for detecting power grid false data injection attacks
  • Method for detecting power grid false data injection attacks

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[0034] Describe the technical scheme of the present invention in detail below in conjunction with accompanying drawing:

[0035] The fake data injection attack detection model proposed by the present invention is divided into two links: training and detection. The purpose of training is to determine the relevant parameters in the deep neural network to achieve online detection of fake data injection attacks. Therefore, these two links are similar in process. The difference is that the training link is used to first determine the unknown parameters in the deep neural network, and then the trained deep neural network is used for online detection in the detection link, that is, the presence or absence of false data injection is realized. Attack on the binary classification problem.

[0036] figure 1 This is a flow chart of the detection method for fake data injection attack proposed by the present invention. The power grid terminal uploads the collected operating state informa...

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Abstract

The invention discloses a method for detecting power grid false data injection attacks. The method comprises the following steps of: calculating to obtain a system running state estimation value according to data acquired by a power grid terminal, extracting time-frequency characteristic data of each time period from the state estimation value by using wavelet transform, and inputting the time-frequency characteristic data into a deep neural network to classify whether false data injection attacks exist or not, and outputting a detection result. The method can be directly used for detecting the state estimation result of the power system, and the detection result can indicate whether the state estimation of the power system is affected by false data injection attacks or not.

Description

technical field [0001] The invention relates to a method for detecting whether there is a false data injection attack in a power grid. Background technique [0002] The core link of power system operation and control is state estimation, which calculates the actual operating state of the power grid through the power grid state information collected in the data acquisition and monitoring system (SCADA). [0003] Malicious network attacks, such as false data injection attacks (false data injection attack, FDIA), etc., can tamper with the measurement data in SCADA, thereby affecting the results of smart grid state estimation, seriously threatening the stable operation of the power grid, and even causing large-scale power outage losses, such as Recent grid attacks in Ukraine and Venezuela. How to detect whether there is a malicious attack on the power grid is a necessary prerequisite for timely taking protective measures to ensure the stable operation of the power grid. [000...

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Application Information

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IPC IPC(8): H04L29/06G06K9/62G06N3/04
CPCH04L63/1416H04L63/1466G06N3/045G06F18/24
Inventor 赵友国蒋正威陶涛张超王立建张静卢敏阙凌燕隋向阳逄春姜辰曹张洁杨帆黄铭郭抒然赵泓张若伊孙伟乐贾和东
Owner STATE GRID ZHEJIANG ELECTRIC POWER CO LTD HANGZHOU POWER SUPPLY CO
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