Black box adversarial sample attack method for electric energy quality signal neural network classification model

A neural network model and power quality technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve problems such as economic loss to the grid, difficulty for attackers to obtain model architecture and parameter information, data loss, etc.

Pending Publication Date: 2020-12-29
AIR FORCE UNIV PLA
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Problems solved by technology

Disturbances of power quality signals can cause serious problems in the power grid: signal disturbances in the power grid may cause economic losses as well as failure and damage of equipment in the power grid; in end-user systems, power signal disturbances may cause data loss, sensitive loads ( Memory failures such as computers, protection and relay equipment) and instability of controls
However, adversarial attacks against neural n

Method used

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  • Black box adversarial sample attack method for electric energy quality signal neural network classification model
  • Black box adversarial sample attack method for electric energy quality signal neural network classification model
  • Black box adversarial sample attack method for electric energy quality signal neural network classification model

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Embodiment Construction

[0054] Such as figure 1 As shown, the power quality signals collected in the power system are transmitted to the designated signal classification module through the communication network. The signal classification module realizes the correct classification of signals by the trained deep learning (neural network) model, and according to the predicted classification results (ie figure 1 The "prediction type" in ) provides corresponding control instructions for the power system control center to adjust the operating state of the power grid. The attacker can invade the communication network of the power system, intercept the power measurement signal of the system, and generate the corresponding countermeasure signal (the difference between the confrontation signal and the original measurement signal is very small, and the system operator cannot detect the change), so that the neural network model can be classified incorrectly. It causes the power system control center to make wro...

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Abstract

The invention relates to a black box adversarial sample attack method for a power quality signal neural network classification model, which is characterized by specifically comprising the following steps: a target model and a local substitution model of an attacker are subjected to training; the attacker generates general disturbance for the local training model; and the attacker attacks the target model by using the generated general disturbance. According to the method, the vulnerability of the neural network and the mobility of the adversarial sample are effectively utilized, the actual situation of attacks is met, and the attack success rate is high.

Description

technical field [0001] The present invention relates to the application of network security and deep learning in the classification of power quality signals. Perturbation and on this basis, implement adversarial sample black-box attacks against the target neural network model. Background technique [0002] Power quality refers to the various electromagnetic phenomena that characterize the voltage and current measured at a specific time and at a specific location in a power system. The disturbance of the power quality signal can cause serious problems in the power grid: the signal disturbance occurring in the power grid may cause economic loss and the failure and damage of equipment in the power grid; in the end-user system, the power signal disturbance may cause data loss, sensitive load ( Such as computer, protection and relay equipment) memory failure and control instability. Therefore, if the monitoring system cannot correctly identify and eliminate power signal interfe...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06Q10/06G06Q50/06
CPCG06N3/08G06Q10/06395G06Q50/06G06N3/045G06F2218/12G06F18/241
Inventor 田继伟王布宏郭戎潇魏青梅尚福特曾乐雅
Owner AIR FORCE UNIV PLA
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