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Register a new userVulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation
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Added by
Oliver Kosut
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization problem is introduced whose objective is to maximize the physical line flows subsequent to an FDI attack on DC SE. The maximization is subject to constraints on both attacker resources (size of attack) and attack detection (limiting load shifts) as well as those required by DC optimal power flow (OPF) following SE. The resulting attacks are tested on a more realistic non-linear system model using AC state estimation and ACOPF, and it is shown that, with an appropriately chosen sub-network, the attacker can overload transmission lines with moderate shifts of load.
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Power systems are moving towards hybrid AC/DC grids with the integration of HVDC links, renewable resources and energy storage modules. New models of frequency control have to consider the complex interactions between these components. Meanwhile, more attention should be paid to cyber security concerns as these control strategies highly depend on data communications which may be exposed to cyber attacks. In this regard, this article aims to analyze the false data injection (FDI) attacks on the AC/DC interconnected system with virtual inertia and develop advanced diagnosis tools to reveal their occurrence. We build an optimization-based framework for the purpose of vulnerability and attack impact analysis. Considering the attack impact on the system frequency stability, it is shown that the hybrid grid with parallel AC/DC links and emulated inertia is more vulnerable to the FDI attacks, compared with the one without virtual inertia and the normal AC system. We then propose a detection approach to detect and isolate each FDI intrusion with a sufficient fast response, and even recover the attack value. In addition to theoretical results, the effectiveness of the proposed methods is validated through simulations on the two-area AC/DC interconnected system with virtual inertia emulation capabilities.
A novel false data injection attack (FDIA) model against DC state estimation is proposed, which requires no network parameters and exploits only limited phasor measurement unit (PMU) data. The proposed FDIA model can target specific states and launch large deviation attacks using estimated line parameters. Sufficient conditions for the proposed method are also presented. Different attack vectors are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection (BDD) with high success rates of up to 95.3%.
We consider a class of malicious attacks against remote state estimation. A sensor with limited resources adopts an acknowledgement (ACK)-based online power schedule to improve the remote state estimation performance. A malicious attacker can modify the ACKs from the remote estimator and convey fake information to the sensor. When the capability of the attacker is limited, we propose an attack strategy for the attacker and analyze the corresponding effect on the estimation performance. The possible responses of the sensor are studied and a condition for the sensor to discard ACKs and switch from online schedule to offline schedule is provided.
Applying security as a lifecycle practice is becoming increasingly important to combat targeted attacks in safety-critical systems. Among others there are two significant challenges in this area: (1) the need for models that can characterize a realistic system in the absence of an implementation and (2) an automated way to associate attack vector information; that is, historical data, to such system models. We propose the cybersecurity body of knowledge (CYBOK), which takes in sufficiently characteristic models of systems and acts as a search engine for potential attack vectors. CYBOK is fundamentally an algorithmic approach to vulnerability exploration, which is a significant extension to the body of knowledge it builds upon. By using CYBOK, security analysts and system designers can work together to assess the overall security posture of systems early in their lifecycle, during major design decisions and before final product designs. Consequently, assisting in applying security earlier and throughout the systems lifecycle.
A class of data integrity attack, known as false data injection (FDI) attack, has been studied with a considerable amount of work. It has shown that with perfect knowledge of the system model and the capability to manipulate a certain number of measurements, the FDI attacks can coordinate measurements corruption to keep stealth against the bad data detection. However, a more realistic attack is essentially an attack with limited adversarial knowledge of the system model and limited attack resources due to various reasons. In this paper, we generalize the data attacks that they can be pure FDI attacks or combined with availability attacks (e.g., DoS attacks) and analyze the attacks with limited adversarial knowledge or limited attack resources. The attack impact is evaluated by the proposed metrics and the detection probability of attacks is calculated using the distribution property of data with or without attacks. The analysis is supported with results from a power system use case. The results show how important the knowledge is to the attacker and which measurements are more vulnerable to attacks with limited resources.
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