Grid application layer instruction level detection method for converged control service logic

CN117278265BActive Publication Date: 2026-06-23STATE GRID FUJIAN ELECTRIC POWER CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-09-15
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively identify command-level attacks on smart grid terminals and cannot meet high real-time requirements, especially in complex industrial environments where data distribution is difficult to model, resulting in insufficient attack detection.

Method used

A rule-based matching method is used to detect malformed and attack packets. Combined with anomaly detection technology based on clustering learning, the K-Means clustering algorithm is used to classify power grid business command behavior, construct a business behavior model, and realize the anomaly identification of business command frequency and feature code.

Benefits of technology

It enables real-time identification and classification of command-level attacks on smart power grid terminals, improving the security and real-time detection capabilities of the power grid system, and effectively identifying abnormal data in complex environments.

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Abstract

The application relates to a power grid application layer instruction level detection method fusing control service logic. For power grid application layer instruction level detection, a rule matching-based method is used to detect abnormal messages and attack messages; abnormal message rules are designed according to protocol specifications, and a mature snort network attack rule library is used as an attack message feature library; for the discrete characteristics of service feature values, an abnormal detection technology based on clustering learning is proposed to realize abnormal identification of service instruction frequencies and service feature codes.
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Citation Information

Patent Citations

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  • Method and system for improving monitoring accuracy of network attack behavior of power system

    CN116346434A