1,424 research outputs found
Cyber-Physical Systems Security: a Systematic Mapping Study
Cyber-physical systems are integrations of computation, networking, and
physical processes. Due to the tight cyber-physical coupling and to the
potentially disrupting consequences of failures, security here is one of the
primary concerns. Our systematic mapping study sheds some light on how security
is actually addressed when dealing with cyber-physical systems. The provided
systematic map of 118 selected studies is based on, for instance, application
fields, various system components, related algorithms and models, attacks
characteristics and defense strategies. It presents a powerful comparison
framework for existing and future research on this hot topic, important for
both industry and academia.Comment: arXiv admin note: text overlap with arXiv:1205.5073 by other author
Vulnerabilities of Smart Grid State Estimation against False Data Injection Attack
In recent years, Information Security has become a notable issue in the
energy sector. After the invention of The Stuxnet worm in 2010, data integrity,
privacy and confidentiality has received significant importance in the
real-time operation of the control centres. New methods and frameworks are
being developed to protect the National Critical Infrastructures like energy
sector. In the recent literatures, it has been shown that the key real-time
operational tools (e.g., State Estimator) of any Energy Management System (EMS)
are vulnerable to Cyber Attacks. In this chapter, one such cyber attack named
False Data Injection Attack is discussed. A literature review with a case study
is considered to explain the characteristics and significance of such data
integrity attacks.Comment: Renewable Energy Integration, Green Energy and Technology, Springer,
201
Smart False Data Injection attacks against State Estimation in Power Grid
In this paper a new class of cyber attacks against state estimation in the
electric power grid is considered. This class of attacks is named false data
injection attacks. We show that with the knowledge of the system configuration
an attacker could successfully inject false data into certain state variable
while bypassing existing techniques for bad data detection. In the preliminary
section we consider the feasibility of such an attack and the necessary
condition to successfully avoid detection. After that we show that with the
knowledge of the system configuration, certain line flow measurements could be
manipulated to lead to profitable misconduct. By controlling Regional
Transmission Organizations (RTOs) view of system power flow and congestion, an
attacker could manipulate the LMPs of targeted buses according to prior
biddings. Also, in this paper we show the implementation of the false data
injection attacks. The numerical example considered was applied to a malicious
data detection algorithm that was designed on a microcontroller. The results
demonstrated the effectiveness of injecting false data measurements into the
state estimation of electric power grids
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
This paper presents a review of the literature on State Estimation (SE) in
power systems. While covering some works related to SE in transmission systems,
the main focus of this paper is Distribution System State Estimation (DSSE).
The paper discusses a few critical topics of DSSE, including mathematical
problem formulation, application of pseudo-measurements, metering instrument
placement, network topology issues, impacts of renewable penetration, and
cyber-security. Both conventional and modern data-driven and probabilistic
techniques have been reviewed. This paper can provide researchers and utility
engineers with insights into the technical achievements, barriers, and future
research directions of DSSE
Data Attacks on Power System State Estimation: Limited Adversarial Knowledge vs. Limited Attack Resources
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.Comment: Accepted in the 43rd Annual Conference of the IEEE Industrial
Electronics Society (IECON 2017
Statistical Structure Learning, Towards a Robust Smart Grid
Robust control and maintenance of the grid relies on accurate data. Both PMUs
and state estimators are prone to false data injection attacks. Thus, it is
crucial to have a mechanism for fast and accurate detection of an agent
maliciously tampering with the data---for both preventing attacks that may lead
to blackouts, and for routine monitoring and control tasks of current and
future grids. We propose a decentralized false data injection detection scheme
based on Markov graph of the bus phase angles. We utilize the Conditional
Covariance Test (CCT) to learn the structure of the grid. Using the DC power
flow model, we show that under normal circumstances, and because of
walk-summability of the grid graph, the Markov graph of the voltage angles can
be determined by the power grid graph. Therefore, a discrepancy between
calculated Markov graph and learned structure should trigger the alarm. Local
grid topology is available online from the protection system and we exploit it
to check for mismatch. Should a mismatch be detected, we use correlation
anomaly score to detect the set of attacked nodes. Our method can detect the
most recent stealthy deception attack on the power grid that assumes knowledge
of bus-branch model of the system and is capable of deceiving the state
estimator, damaging power network observatory, control, monitoring, demand
response and pricing schemes. Specifically, under the stealthy deception
attack, the Markov graph of phase angles changes. In addition to detect a state
of attack, our method can detect the set of attacked nodes. To the best of our
knowledge, our remedy is the first to comprehensively detect this sophisticated
attack and it does not need additional hardware. Moreover, our detection scheme
is successful no matter the size of the attacked subset. Simulation of various
power networks confirms our claims
EXPOSE the Line Failures following a Cyber-Physical Attack on the Power Grid
Recent attacks on power grids demonstrated the vulnerability of the grids to
cyber and physical attacks. To analyze this vulnerability, we study
cyber-physical attacks that affect both the power grid physical infrastructure
and its underlying Supervisory Control And Data Acquisition (SCADA) system. We
assume that an adversary attacks an area by: (i) disconnecting some lines
within that area, and (ii) obstructing the information (e.g., status of the
lines and voltage measurements) from within the area to reach the control
center. We leverage the algebraic properties of the AC power flows to introduce
the efficient EXPOSE Algorithm for detecting line failures and recovering
voltages inside that attacked area after such an attack. The EXPOSE Algorithm
outperforms the state-of-the-art algorithm for detecting line failures using
partial information under the AC power flow model in terms of scalability and
accuracy. The main advantages of the EXPOSE Algorithm are that its running time
is independent of the size of the grid and number of line failures, and that it
provides accurate information recovery under some conditions on the attacked
area. Moreover, it approximately recovers the information and provides the
confidence of the solution when these conditions do not hold
Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning
Modern advances in sensor, computing, and communication technologies enable
various smart grid applications. The heavy dependence on communication
technology has highlighted the vulnerability of the electricity grid to false
data injection (FDI) attacks that can bypass bad data detection mechanisms.
Existing mitigation in the power system either focus on redundant measurements
or protect a set of basic measurements. These methods make specific assumptions
about FDI attacks, which are often restrictive and inadequate to deal with
modern cyber threats. In the proposed approach, a deep learning based framework
is used to detect injected data measurement. Our time-series anomaly detector
adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM)
network. To effectively estimate system variables, our approach observes both
data measurements and network level features to jointly learn system states.
The proposed system is tested on IEEE 39-bus system. Experimental analysis
shows that the deep learning algorithm can identify anomalies which cannot be
detected by traditional state estimation bad data detection
Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation
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.Comment: 9 pages, 7 figures. A version of this manuscript was submitted to the
IEEE Transactions on Power System
Enhancing Power System Cyber-Security with Systematic Two-Stage Detection Strategy
State estimation estimates the system condition in real-time and provides a
base case for other energy management system (EMS) applications including
real-time contingency analysis and security-constrained economic dispatch.
Recent work in the literature shows malicious cyber-attack can inject false
measurements that bypass traditional bad data detection in state estimation and
cause actual overloads. Thus, it is very important to detect such cyber-attack.
In this paper, multiple metrics are proposed to monitor abnormal load
deviations and suspicious branch flow changes. A systematic two-stage approach
is proposed to detect false data injection (FDI) cyber-attack. The first stage
determines whether the system is under attack while the second stage identifies
the target branch. Numerical simulations verify that FDI can cause severe
system violations and demonstrate the effectiveness of the proposed two-stage
FDI detection (FDID) method. It is concluded that the proposed FDID approach
can efficiently detect FDI cyber-attack and identify the target branch, which
will substantially improve operators situation awareness in real-time.Comment: 11 pages, 15 figure
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