面向电网安全的虚假数据注入攻击动态检测模型

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI:10.1109/AINIT54228.2021.00069

Fuhong Chang, Qi Li, Yuanyuan Wang, Wenfeng Zhang

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引用次数: 0

摘要

为了保障电网的安全，提高虚假数据注入的预警精度。提出了一种针对虚假数据注入攻击的动态检测模型。根据APT攻击的特点，构造了可信区域的攻击特征模型。为了实现准确的状态估计，采用无气味卡尔曼滤波算法对非线性电力系统进行状态估计，实现动态攻击检测。实验结果表明，该方法的准确率高于90%，验证了本文在攻击检测中的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Dynamic Detection Model of False Data Injection Attack Facing Power Network Security

In order to protect the safety of power grid, improve the early warning precision of false data injection. This paper presents a dynamic detection model for false data injection attacks. Based on the characteristics of APT attacks, a model of attack characteristics for trusted regions is constructed. In order to realize the accurate state estimation, unscented Kalman filtering algorithm is used to estimate the state of nonlinear power system and realize dynamic attack detection. Experimental results show that the precision of this method is higher than 90%, which verifies the effectiveness of this paper in attack detection.

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来源期刊

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

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