基于ASRUKF和IMC算法的电力信息物理系统虚假数据注入攻击检测
False Data Injection Attack Detection of Cyber-physical Power System Based on ASRUKF and IMC Algorithms
庞清乐 1韩松易 1周泰 1张峰 1焦绪国 1王言前1
作者信息
- 1. 青岛理工大学信息与控制工程学院,山东青岛 266520
- 折叠
摘要
针对传统电力信息物理系统的虚假数据注入攻击(FDIA)检测精度低问题,提出了基于自适应Sage-Husa噪声无迹卡尔曼滤波(ASRUFK)和改进马尔科夫链(IMC)预测算法的电力信息物理系统FDIA检测方法.首先,通过加入随机插值调整状态区间,提出了一种改进马尔科夫链(IMC)预测算法;然后,利用ASRUKF和IMC预测算法分别对系统待测数据进行状态估计,根据2种预测算法状态估计结果的偏差值构建随机变量,利用Box-Cox将随机变量转换为服从正态分布的变量;最后,通过双边假设检验实现电力信息物理系统的FDIA检测.在IEEE-14节点和IEEE-30节点系统中验证了所提出方法的有效性和正确性.
Abstract
To solve the problem of low accuracy of false data injection attack(FDIA)detection in traditional power information physical system,a new FDIA detection method based on adaptive Sage-Husa unscented Kalman filter(ASRUFK)and improved Markov chain(IMC)prediction algorithm is proposed.Firstly,an improved Markov chain(IMC)prediction algorithm is proposed by adding random interpolation to adjust the state interval.Secondly,ASRUKF and IMC prediction algorithms are used to estimate the state of the data to be measured in the system,and random variables are constructed according to the deviation values of the state estimation results of the two prediction algorithms.Box-Cox is used to convert random variables into variables with normal distribution.Finally,the FDIA detection of the power information physical system is realized through bilateral hypothesis testing.The validity and correctness of the proposed method are verified in IEEE-14 node and IEEE-30 node systems.
关键词
电力信息物理系统/虚假数据注入攻击/改进马尔科夫链预测算法/Box-Cox变换/攻击检测Key words
power cyber-physical system/false data injection attack/improved Markov chain prediction algorithm/Box-Cox transformation/attack detection引用本文复制引用
基金项目
国家自然科学基金资助项目(62203249)
山东省自然科学基金资助项目(ZR2021QF115)
出版年
2024
NETL
NSTL
万方数据