11,563 research outputs found
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
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
False Analog Data Injection Attack Towards Topology Errors: Formulation and Feasibility Analysis
In this paper, we propose a class of false analog data injection attack that
can misguide the system as if topology errors had occurred. By utilizing the
measurement redundancy with respect to the state variables, the adversary who
knows the system configuration is shown to be capable of computing the
corresponding measurement value with the intentionally misguided topology. The
attack is designed such that the state as well as residue distribution after
state estimation will converge to those in the system with a topology error. It
is shown that the attack can be launched even if the attacker is constrained to
some specific meters. The attack is detrimental to the system since
manipulation of analog data will lead to a forged digital topology status, and
the state after the error is identified and modified will be significantly
biased with the intended wrong topology. The feasibility of the proposed attack
is demonstrated with an IEEE 14-bus system.Comment: 5 pages, 7 figures, Proc. of 2018 IEEE Power and Energy Society
General Meetin
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Deep neural networks are vulnerable to adversarial attacks. The literature is
rich with algorithms that can easily craft successful adversarial examples. In
contrast, the performance of defense techniques still lags behind. This paper
proposes ME-Net, a defense method that leverages matrix estimation (ME). In
ME-Net, images are preprocessed using two steps: first pixels are randomly
dropped from the image; then, the image is reconstructed using ME. We show that
this process destroys the adversarial structure of the noise, while
re-enforcing the global structure in the original image. Since humans typically
rely on such global structures in classifying images, the process makes the
network mode compatible with human perception. We conduct comprehensive
experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and
Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows
that ME-Net consistently outperforms prior techniques, improving robustness
against both black-box and white-box attacks.Comment: ICML 201
Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise
In this paper, we consider the problems of state estimation and false data
injection detection in smart grid when the measurements are corrupted by
colored Gaussian noise. By modeling the noise with the autoregressive process,
we estimate the state of the power transmission networks and develop a
generalized likelihood ratio test (GLRT) detector for the detection of false
data injection attacks. We show that the conventional approach with the
assumption of Gaussian noise is a special case of the proposed method, and thus
the new approach has more applicability. {The proposed detector is also tested
on an independent component analysis (ICA) based unobservable false data attack
scheme that utilizes similar assumptions of sample observation.} We evaluate
the performance of the proposed state estimator and attack detector on the IEEE
30-bus power system with comparison to conventional Gaussian noise based
detector. The superior performance of {both observable and unobservable false
data attacks} demonstrates the effectiveness of the proposed approach and
indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network
Security (CNS) 201
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