电力系统自动化2017，Vol.41Issue(11)：26-32,7.
基于拉格朗日乘子法的虚假数据攻击策略
Injected Attack Strategy for False Data Based on Lagrange Multipliers Method
摘要
Abstract
As there may be errors and incompleteness in electric parameters mastered by attackers, and bad data may also exist in measurements, an injected attack strategy for false data based on Lagrange multipliers method is proposed.First, bad data is identified and unknown branch impedance is estimated by the Lagrange multipliers method and augmented state estimation method, respectively.Then, in the convex relaxation framework, the classic false data injected attack model aimed at attacking an arbitrary specific measurement is transformed into a basis-pursuit (BP) model.Finally, the suboptimum attack vector is quickly solved by alternating the direction method of multipliers (ADMM).In order to evaluate the strategy, simulations are tested in typical IEEE bus test systems.The results show that ADMM is more efficient than classic linear programming (LP) based on the BP model when the classic false data injected attack model is transformed into a BP model.It is also found that when the number of impedance unknown branches is small, the success rate is relatively high.But when the standard deviation of error vector for state variables decreases, the effect of the quantity of impedance unknown branches on the success rate will be weak.Moreover, this strategy does not significantly increase the attack cost.关键词
虚假数据攻击/信息物理融合系统/拉格朗日乘子法/基追踪模型/交替方向乘子法Key words
false data injection attacks/cyber-physical systems/Lagrange multipliers method/basis-pursuit(BP)model/alternating direction method of multipliers(ADMM)引用本文复制引用
田猛,王先培,董政呈,朱国威,代荡荡,赵乐..基于拉格朗日乘子法的虚假数据攻击策略[J].电力系统自动化,2017,41(11):26-32,7.基金项目
国家自然科学基金资助项目(50677047) （50677047）
湖北省自然科学基金资助项目(2015CFB563) （2015CFB563）
中央高校基本科研业务费专项资金资助项目(2042017kf0037) （2042017kf0037）
This work is supported by National Natural Science Foundation of China (No.50677047), Hubei Provincial Natural Science Foundation of China (No.2015CFB563) and Fundamental Research Funds for the Central Universities (No.2042017kf0037). （No.50677047）
