7,376 research outputs found
Local Cyber-physical Attack with Leveraging Detection in Smart Grid
A well-designed attack in the power system can cause an initial failure and
then results in large-scale cascade failure. Several works have discussed power
system attack through false data injection, line-maintaining attack, and
line-removing attack. However, the existing methods need to continuously attack
the system for a long time, and, unfortunately, the performance cannot be
guaranteed if the system states vary. To overcome this issue, we consider a new
type of attack strategy called combinational attack which masks a line-outage
at one position but misleads the control center on line outage at another
position. Therefore, the topology information in the control center is
interfered by our attack. We also offer a procedure of selecting the vulnerable
lines of its kind. The proposed method can effectively and continuously deceive
the control center in identifying the actual position of line-outage. The
system under attack will be exposed to increasing risks as the attack
continuously. Simulation results validate the efficiency of the proposed attack
strategy.Comment: Accepted by IEEE SmartGridComm 201
Local Cyber-Physical Attack for Masking Line Outage and Topology Attack in Smart Grid
Malicious attacks in the power system can eventually result in a large-scale
cascade failure if not attended on time. These attacks, which are traditionally
classified into \emph{physical} and \emph{cyber attacks}, can be avoided by
using the latest and advanced detection mechanisms. However, a new threat
called \emph{cyber-physical attacks} which jointly target both the physical and
cyber layers of the system to interfere the operations of the power grid is
more malicious as compared with the traditional attacks. In this paper, we
propose a new cyber-physical attack strategy where the transmission line is
first physically disconnected, and then the line-outage event is masked, such
that the control center is misled into detecting as an obvious line outage at a
different position in the local area of the power system. Therefore, the
topology information in the control center is interfered by our attack. We also
propose a novel procedure for selecting vulnerable lines, and analyze the
observability of our proposed framework. Our proposed method can effectively
and continuously deceive the control center into detecting fake line-outage
positions, and thereby increase the chance of cascade failure because the
attention is given to the fake outage. The simulation results validate the
efficiency of our proposed attack strategy.Comment: accepted by IEEE Transactions on Smart Grid. arXiv admin note: text
overlap with arXiv:1708.0320
Statistical Methods for Detection and Mitigation of the Effect of Different Types of Cyber-Attacks and Inconsistencies in Electrical Design Parameters in a Real World Distribution System
In the present grid real time control systems are the energy management systems and distribution management systems that utilize measurements from real-time units (RTUs) and Supervisory Control and Data Acquisition (SCADA). The SCADA systems are designed to operate on isolated, private networks without even basic security features which are now being migrated to modern IP-based communications providing near real time information from measuring and controlling units. To function brain (SCADA) properly heart (RTUs) should provide necessary response thereby creating a coupling which makes SCADA systems as targets for cyber-attacks to cripple either part of the electric transmission grid or fully shut down (create blackout) the grid. Cyber-security research for a distribution grid is a topic yet to be addressed. To date firewalls and classic signature-based intrusion detection systems have provided access control and awareness of suspicious network traffic but typically have not offered any real-time detection and defense solutions for electric distribution grids.;This thesis work not only addresses the cyber security modeling, detection and prevention but also addresses model inconsistencies for effectively utilizing and controlling distribution management systems. Inconsistencies in the electrical design parameters of the distribution network or cyber-attack conditions may result in failing of the automated operations or distribution state estimation process which might lead the system to a catastrophic condition or give erroneous solutions for the probable problems. This research work also develops a robust and reliable voltage controller based on Multiple Linear Regression (MLR) to maintain the voltage profile in a smart distribution system under cyber-attacks and model inconsistencies. The developed cyber-attack detection and mitigation algorithms have been tested on IEEE 13 node and 600+ node real American electric distribution systems modeled in Electric Power Research Institute\u27s (EPRI) OpenDSS software
Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts
Smart Grid has rapidly transformed the centrally controlled power system into
a massively interconnected cyber-physical system that benefits from the
revolutions happening in the communications (e.g. 5G) and the growing
proliferation of the Internet of Things devices (such as smart metres and
intelligent electronic devices). While the convergence of a significant number
of cyber-physical elements has enabled the Smart Grid to be far more efficient
and competitive in addressing the growing global energy challenges, it has also
introduced a large number of vulnerabilities culminating in violations of data
availability, integrity, and confidentiality. Recently, false data injection
(FDI) has become one of the most critical cyberattacks, and appears to be a
focal point of interest for both research and industry. To this end, this paper
presents a comprehensive review in the recent advances of the FDI attacks, with
particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts
in the Smart Grid infrastructure. This review paper aims to provide a thorough
understanding of the incumbent threats affecting the entire spectrum of the
Smart Grid. Related literature are analysed and compared in terms of their
theoretical and practical implications to the Smart Grid cybersecurity. In
conclusion, a range of technical limitations of existing false data attack
research is identified, and a number of future research directions is
recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl
Vulnerability Analysis of False Data Injection Attacks on Supervisory Control and Data Acquisition and Phasor Measurement Units
abstract: The electric power system is monitored via an extensive network of sensors in tandem with data processing algorithms, i.e., an intelligent cyber layer, that enables continual observation and control of the physical system to ensure reliable operations. This data collection and processing system is vulnerable to cyber-attacks that impact the system operation status and lead to serious physical consequences, including systematic problems and failures.
This dissertation studies the physical consequences of unobservable false data injection (FDI) attacks wherein the attacker maliciously changes supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) measurements, on the electric power system. In this context, the dissertation is divided into three parts, in which the first two parts focus on FDI attacks on SCADA and the last part focuses on FDI attacks on PMUs.
The first part studies the physical consequences of FDI attacks on SCADA measurements designed with limited system information. The attacker is assumed to have perfect knowledge inside a sub-network of the entire system. Two classes of attacks with different assumptions on the attacker's knowledge outside of the sub-network are introduced. In particular, for the second class of attacks, the attacker is assumed to have no information outside of the attack sub-network, but can perform multiple linear regression to learn the relationship between the external network and the attack sub-network with historical data. To determine the worst possible consequences of both classes of attacks, a bi-level optimization problem wherein the first level models the attacker's goal and the second level models the system response is introduced.
The second part of the dissertation concentrates on analyzing the vulnerability of systems to FDI attacks from the perspective of the system. To this end, an off-line vulnerability analysis framework is proposed to identify the subsets of the test system that are more prone to FDI attacks.
The third part studies the vulnerability of PMUs to FDI attacks. Two classes of more sophisticated FDI attacks that capture the temporal correlation of PMU data are introduced. Such attacks are designed with a convex optimization problem and can always bypass both the bad data detector and the low-rank decomposition (LD) detector.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
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