Can Attackers With Limited Information Exploit Historical Data to Mount Successful False Data Injection Attacks on Power Systems?

Abstract

This paper studies physical consequences of unobservable false data injection (FDI) attacks designed only with information inside a sub-network of the power system. The goal of this attack is to overload a chosen target line without being detected via measurements. To overcome the limited information, a multiple linear regression model is developed to learn the relationship between the external network and the attack subnetwork from historical data. The worst possible consequences of such FDI attacks are evaluated by solving a bi-level optimization problem wherein the first level models the limited attack resources, while the second level formulates the system response to such attacks via DC optimal power flow (OPF). The attack model with limited information is reflected in the DC OPF formulation that only takes into account the system information for the attack sub-network. The vulnerability of this attack model is illustrated on the IEEE 24-bus RTS and IEEE 118-bus systems.

False data injection attacks on phasor measurements that bypass low-rank decomposition

“…In fact, they are stronger than Theorem 1 since we did not find any case where the LD detector results in C * (LD) such that C * (LD) = 0 and supp C * (LD) ⊆ I. (To Side) 1, 3,4,8,9,12,13,14,15,18,19,21,22,27,31,32,35,36,45,47,48,50,51,56,61,65,66,67,68,69,73,78,79,91,92,94,111,112,113,115,116,117,118,119,120,123,124,125,127,131,132,134,140,145,146,160,166,168,174,…”

“…Recently in [25]- [27], low-rank decomposition (LD) has been proposed to detect FDI attacks on the electric power system using a block of consecutive measurement data. On the other hand, the FDI attacks of most interest are those in which the attacker is not omniscient and omnipresentthis limited knowledge and limited capabilities of FDI attacks are often captured (see, for e.g., [12], [13], [18], [19], [28]- [31]) by restricting attacker knowledge to a subset of the network and restricting counterfeits to a small number of meters, respectively. This latter restriction along with the above mentioned low-rank properties of a block of PMU data suggests that the resulting counterfeit PMU measurement matrix can be viewed as a linear combination of a lowrank (actual) measurement matrix and a sparse attack matrix (counterfeit additions to measurement).…”

Intelligent data attacks against power systems using incomplete network information: a review

“…The authors in [34] introduced a practical attack scheme using limited network information. Considering the limited information of the attacker, a multiple linear regression model was introduced to learn the relationship between the attack region and the outer subnetwork based on historical data.…”

Modelling and Vulnerability Analysis of Cyber-Physical Power Systems Based on Interdependent Networks

“…The power grid belongs to a time-varying and continuous system, while the communication system is a discrete system. There are obvious differences between them in terms of components, transmission content and working mechanisms [5]. Research on the interaction between the cyber layer and physical layer contributes to reveal the propagation law of cascading fault in CPPS.…”