Service scene disposal risk evaluation method and system for power monitoring system

Abstract

The invention discloses a service scene disposal risk evaluation method and system for a power monitoring system. The method comprises the steps of obtaining abnormal behavior data of an unknown user;determining a plurality of suggested disposal strategies for the abnormal behavior data of the unknown user according to a historical judgment process; extracting risk characteristic data through unknown user abnormal behavior data, matching the risk characteristic data with the suggested disposal strategy, and judging a corresponding risk level and a risk estimation value of the user abnormal behavior; inputting the corresponding risk level and the risk pre-estimated value of the abnormal behavior of the user into a neural network model of SVM-KNN supervised learning and K-means unsupervisedlearning; and respectively participating in operation on the classified samples and the non-classified samples to obtain a risk assessment index value and a comprehensive assessment core index scoreof each user abnormal behavior. The method and system have the advantages that the neural network models of SVM-KNN supervised learning and K-means unsupervised learning are introduced, and stable, efficient and safe operation of a power monitoring system is guaranteed.

Description

technical field [0001] The invention relates to a risk assessment method for handling business scenarios of a power monitoring system, which belongs to the technical field of power. Background technique [0002] At present, the network security situation is becoming more and more severe, and network attacks are developing in a precise direction. Smart hackers can use social engineering and other means to obtain internal user permissions, and use internal user identities to conduct precise attacks on internal important business systems. Compared with hackers breaking through layers of security protection methods from the physical boundary of the organization to enter the organization's network, internal users can naturally avoid the security protection capabilities of the physical boundary of the organization and have direct access to the core data and assets within the organization. In the power monitoring system, both the main station and the factory station have deployed c...

AI Technical Summary

Problems solved by technology

[0004] At present, domestic and foreign disposal risk assessments all adopt quantitative or qualitative methods, ignoring the comprehensive impact of indicators of different dimensions on the disposal effect in terms of selection of risk assessment elements and consideration of risk change factors, and the inaccuracy of the indicator data itself is not considered and ambiguity, resulting in one-sidedness and inaccuracy in the assessment

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