Power grid security risk prediction method

Abstract

The invention relates to a power grid security risk prediction method. The method comprises steps that screened network security posture factors are processed; security posture state classification standards are determined; a hidden Markov model is constructed according to actual power grid conditions; hidden Markov model parameters are updated according to present security posture of equipment; security posture of an integral electric power network is calculated; a prediction risk value of the equipment and a weight prediction risk value are calculated. Through the method, the network security posture factors are deeply researched, the factors having greatest influence on the security posture are screened out, data processing workload is reduced, model training is carried out through utilizing the data relevant to the security posture factors, a problem of over-hugeness of a hidden Markov transition matrix is effectively alleviated, moreover, problems of non-high prediction precision and parameter determination difficulty are alleviated.

Description

technical field [0001] The invention relates to a safety risk prediction method, in particular to a power grid safety risk prediction method. Background technique [0002] At present, my country's information security construction is also advancing with the development of technology, but the number of people who are harmed by the Internet is also increasing year by year. From the perspective of the national information security situation, at present, the basic, overall and all-member role of the information system is increasing day by day. As an important guarantee for the in-depth promotion of informatization, information security has become an important part of the national security strategy. The state has successively promulgated the "2006-2020 National Informatization Development Strategy", "Notice on Strengthening Information Security Management of Industrial Control Systems" (2011); "(2012), the purpose is to enhance the security protection capabilities of important i...

AI Technical Summary

Problems solved by technology

The gray prediction method is difficult to use and has a good prediction effect on linear data, but the prediction accuracy is slightly insufficient and cannot reflect the actual situation of the network and other issues; the network security situation assessment method based on immune theory can only reflect the trend of security situation The real-time accuracy of network security situation prediction needs to be improved; the network security situation prediction method based on likelihood BP uses the situation evaluation model to establish a situation sequence as a training sequence, but the parameter training process of this method is complicated, the convergence speed is slow, and it cannot meet the real-time requirements ; The situation prediction method based on RBF neural network uses RBF neural network to process nonlinear situation values, and predicts the situation through the relationship between situation values. However, this method is prone to fall into local optimization problems in real-time network situation awareness, and may It leads to unstable results; the hidden Markov model is widely used in the field of network security, but this method has the disadvantage of a large state transition matrix

[0005] Both gray prediction and hidden Markov prediction are widely used in prediction, but they also have obvious weaknesses. Gray prediction requires less data for prediction, but the prediction accuracy is also low, which cannot reflect the actual network. The actual situation

Hidden Markov model can be used to predict network security situation, but there is a problem that the state transition matrix is ​​too large

[0006] However, the traditional network security situation prediction method only analyzes the data of the network security situation itself, and uses data mining technology to mine the change law of the security situation data sequence to achieve the purpose of prediction. There is no in-depth study on the network security situation factors, and the prediction accuracy is not good. High, difficult to determine parameters and other issues

[0007] Therefore, there is an urgent need for a power grid security risk prediction method, which combines the advantages of gray prediction and hidden Markov model prediction methods to solve the problems of low prediction accuracy and difficult parameter determination.

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