Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. Recent studies have shown that an attacker can inject pre-determined false data into smart meters such that it can pass the residue test of conventional state estimator. However, the calculation of the false data vector relies on the network (topology and parameter) information of the entire grid. In practice, it is impossible for an attacker to obtain all network information of a power grid. Unfortunately, this does not make power systems immune to false data injection attacks. In this paper, we propose a local load redistribution attacking model based on incomplete network information and show that an attacker only needs to obtain the network information of the local attacking region to inject false data into smart meters in the local region without being detected by the state estimator. Simulations on the modified IEEE 14-bus system demonstrate the correctness and effectiveness of the proposed model. The results of this paper reveal the mechanism of local false data injection attacks and highlight the importance and complexity of defending power systems against false data injection attacks.

Local Load Redistribution Attacks in Power Systems With Incomplete Network Information

Modern power grids are becoming more prone to cyberattacks. Even worse, an attacker without the full topology and parameter information of a power grid can still execute a false data injection attack without being detected by the state estimator. This paper proposes an efficient strategy for determining the optimal attacking region that requires reduced network information. The effectiveness of the proposed algorithm is verified through extensive simulations. This paper introduces a new front in the study of smart grid cyber security: determination of a feasible attacking region by obtaining less network information. This paper is also essential and significant for finding effective protection strategies against false data injection attacks based on the deep understanding of the mechanisms and strategies of the attacks.

Modeling of Local False Data Injection Attacks With Reduced Network Information

The power grid is becoming more dependent on information and communication technologies. Complex networks of advanced sensors such as phasor measurement units (PMUs) are used to collect real time data to improve the observability of the power system. Recent studies have shown that the power grid has significant cyber vulnerabilities which could increase when PMUs are used extensively. Therefore, recognizing and responding to vulnerabilities are critical to the security of the power grid. This paper proposes a risk mitigation model for optimal response to cyber-attacks to PMU networks. We model the optimal response action as a mixed integer linear programming (MILP) problem to prevent propagation of the cyber-attacks and maintain the observability of the power system.

Jianhui Wang

Papers

Real-time data reassurance in electrical power systems based on artificial neural networks