Power grid security situation awareness platform architecture

Abstract

The invention provides a power grid security situation awareness platform architecture. The power grid security situation awareness platform architecture comprises a security data acquisition and storage module, an intrusion detection module, an intelligent situation analysis module and a situation visualization module, through technologies of active monitoring, flow analysis, enterprise side collection and the like, power Internet of Things security threat perception, attack discovery, violation behavior monitoring, threat alarm and the like are formed, and security situation perception service is provided for the power industry aiming at the characteristics of multiple perception nodes, different types, diversified connections, dynamic and changeable information and the like in the power Internet of Things environment. An electric power Internet of Things security situation awareness solution is formed.

Description

technical field [0001] The invention relates to the technical field of big data applications, in particular to a grid security situation awareness platform architecture. Background technique [0002] With the rapid growth of wind power, solar energy and other new energy power generation, the era of smart energy and power Internet of Things is roaring, and the energy system is transforming into the era of fragmented energy. Fragmented energy will exist in the form of interconnection of all things and highly intelligent maximize its value. The power Internet of Things environment is a network environment for collecting, storing, analyzing, computing, and sharing big data. It belongs to a huge nonlinear complex system. Its complexity is mainly manifested in the huge number of nodes, node diversity, connection diversity, and information diversity. , dynamic complexity, complex and changeable network structure, and the integration of multiple complexities, so the network in the ...

AI Technical Summary

Problems solved by technology

[0006] 2. The factors affecting network security status in the power Internet of Things environment are complex and diverse, and massive and complex data affect the real-time performance of data fusion and event correlation analysis

[0007] In the big data environment, there are correlations, mutual influences, and real-time changes among various security feature elements, and it is very difficult to integrate and process information; fusing a large amount of network security feature data may result in incomplete feature data extraction and feature space dimensionality reduction. The effect is difficult to evaluate, which may eventually lead to the induction of inaccurate feature information from the data set

[0008] The data of network security perception in the power Internet of Things environment contains a large amount of uncertain information, which is incomplete, inaccurate, and contradictory to a certain extent. It is necessary to solve the problem of uncertain information in the situation assessment process; the current research on network security situation Indicators are often aimed at a certain aspect or a certain application scenario, lacking an indicator system that describes the overall network security situation assessment in the power Internet of Things environment, and there is no unified evaluation standard

[0009] 3. The existing network security situation awareness model still has shortcomings such as heavy load, large response delay, poor integrity, stability and accuracy

[0010] 4. At present, there is no complete set of situation awareness system under the environment of power Internet of Things in China, which can effectively monitor and evaluate the information security risks of its own industrial control network

Lack of a dynamic network security situation prediction model for the power Internet of Things environment, it is impossible to realize real-time and accurate prediction of the global network security situation in the power Internet of Things environment; the existing network security situation prediction strongly relies on data preprocessing and manual intervention. The learning of historical experience knowledge still lacks intelligence; network security prediction methods need further research in the future in terms of improving learning efficiency, convergence speed, and prediction accuracy; existing network security situation prediction methods are difficult to predict the occurrence of network attacks in the power Internet of Things environment. Time, node location and attack type cannot effectively support the precise decision-making of network security active defense in the power Internet of Things environment

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