scholarly journals A Novel Sparse Attack Vector Construction Method for False Data Injection in Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2940 ◽  
Author(s):  
Meng Xia ◽  
Dajun Du ◽  
Minrui Fei ◽  
Xue Li ◽  
Taicheng Yang
Keyword(s):  
Smart Grids ◽  
Construction Method ◽  
Feasible Region ◽  
Data Detection ◽  
Transformation Theory ◽  
Singular Value Decomposition Method ◽  
False Data Injection ◽  
Construction Methods ◽  
Vector Construction ◽  
Value Decomposition

To improve the security of smart grids (SGs) by finding the system vulnerability, this paper investigates the sparse attack vectors’ construction method for malicious false data injection attack (FDIA). The drawbacks of the existing attack vector construction methods include avoiding discussing the feasible region and validity of the attack vector. For the above drawbacks, this paper has three main contributions: (1) To construct the appropriate attack evading bad data detection (BDD), the feasible region of the attack vector is proved by projection transformation theory. The acquisition of the feasible region can help the defender to formulate the defense strategy; (2) an effective attack is proposed and the constraint of effectiveness is obtained using norm theory; (3) the domain of the state variations caused by the attack vector in the feasible region is calculated, while the singular value decomposition method is adopted. Finally, an attack vector is constructed based on l 0 -norm using OMP algorithms in the feasible domain. Simulation results confirm the feasibility and effectiveness of the proposed technique.

scholarly journals A Novel Sparse False Data Injection Attack Method in Smart Grids with Incomplete Power Network Information

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Huixin Zhong ◽  
Dajun Du ◽  
Chuanjiang Li ◽  
Xue Li
Keyword(s):  
Smart Grids ◽  
Power Flow ◽  
Detection Threshold ◽  
Flow Equation ◽  
Data Detection ◽  
Power Network ◽  
Network Information ◽  
False Data Injection ◽  
L2 Norm ◽  
False Data Injection Attack

The paper investigates a novel sparse false data injection attack method in a smart grid (SG) with incomplete power network information. Most existing methods usually require the known complete power network information of SG. The main objective of this paper is to propose an effective sparse false data injection attack strategy under a more practical situation where attackers can only have incomplete power network information and limited attack resources to access the measurements. Firstly, according to the obtained measurements and power network information, some incomplete power network information is compensated by using the power flow equation approach. Then, the fault tolerance range of bad data detection (BDD) for the attack residual increment is estimated by calculating the detection threshold of the residual L2-norm test. Finally, an effective sparse imperfect strategy is proposed by converting the choice of measurements into a subset selection problem, which is solved by the locally regularized fast recursive (LRFR) algorithm to effectively improve the sparsity of attack vectors. Simulation results on an IEEE 30-bus system and a real distribution network system confirm the feasibility and effectiveness of the proposed new attack construction method.

scholarly journals Stacked Autoencoder Framework of False Data Injection Attack Detection in Smart Grid

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liang Chen ◽  
Songlin Gu ◽  
Ying Wang ◽  
Yang Yang ◽  
Yang Li
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Control Strategies ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Data Detection ◽  
Layer By Layer ◽  
Monitoring And Control ◽  
Bad Data Detection ◽  
False Data Injection

The advanced communication technology provides new monitoring and control strategies for smart grids. However, the application of information technology also increases the risk of malicious attacks. False data injection (FDI) is one kind of cyber attacks, which cannot be detected by bad data detection in state estimation. In this paper, a data-driven FDI attack detection framework of the smart grid with phasor measurement units (PMUs) is proposed. To enhance the detecting accuracy and efficiency, the multiple layer autoencoder algorithm is applied to abstract the hidden features of PMU measurements layer by layer in an unsupervised manner. Then, the features of the measurements and corresponding labels are taken as inputs to learn a softmax layer. Last, the autoencoder and softmax layer are stacked to form a FDI detection framework. The proposed method is applied on the IEEE 39-bus system, and the simulation results show that the FDI attacks can be detected with higher accuracy and computational efficiency compared with other artificial intelligence algorithms.

scholarly journals Tailings facility disclosures reveal stability risks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel M. Franks ◽  
Martin Stringer ◽  
Luis A. Torres-Cruz ◽  
Elaine Baker ◽  
Rick Valenta ◽  
...  
Keyword(s):  
Information Disclosure ◽  
Significant Risk ◽  
Construction Method ◽  
Facility Management ◽  
Alternative Technologies ◽  
Construction Methods ◽  
Comprehensive Survey ◽  
Engineering Standards ◽  
The Right ◽  
Stability Issues

AbstractTailings facility failures represent a significant risk to the environment and communities globally, but until now little data was available on the global distribution of risks and characteristics of facilities to ensure proper governance. We conducted a survey and compiled a database with information on tailings facilities disclosed by extractive companies at the request of institutional investors. Despite limitations in the data, this information disclosure request represents the most comprehensive survey of tailings facilities ever undertaken. The compiled dataset includes 1743 tailings facilities and provides insights into a range of topics including construction method, stability, consequence of failure, stored volume, and the rate of uptake of alternative technologies to dewater tailings and reduce geotechnical risk. Our analysis reveals that 10 per cent of tailings facilities reported notable stability concerns or failure to be confirmed or certified as stable at some point in their history, with distinct trends according to construction method, governance, age, height, volume and seismic hazard. Controversy has surrounded the safety of tailings facilities, most notably upstream facilities, for many years but in the absence of definitive empirical data differentiating the risks of different facility types, upstream facilities have continued to be used widely by the industry and a consensus has emerged that upstream facilities can theoretically be built safely under the right circumstances. Our findings reveal that in practice active upstream facilities report a higher incidence of stability issues (18.3%) than other facility types, and that this elevated risk persists even when these facilities are built in high governance settings. In-pit/natural landform and dry-stack facilities report lower incidence of stability issues, though the rate of stability issues is significant by engineering standards (> 2 per cent) across all construction methods, highlighting the universal importance of careful facility management and governance. The insights reported here can assist the global governance of tailings facility stability risks.

scholarly journals Review of Cyber-Physical Attacks in Smart Grids: A System-Theoretic Perspective

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1153
Author(s):  
Francesco Liberati ◽  
Emanuele Garone ◽  
Alessandro Di Giorgio
Keyword(s):  
Smart Grid ◽  
State Estimation ◽  
Smart Grids ◽  
False Data Injection ◽  
Injection Attacks ◽  
The Common ◽  
Dynamical Properties ◽  
Attack Models

This paper presents a review of technical works in the field of cyber-physical attacks on the smart grid. The paper starts by discussing two reference mathematical frameworks proposed in the literature to model a smart grid under attack. Then, a review of cyber-physical attacks on the smart grid is presented, starting from works on false data injection attacks against state estimation. The aim is to present a systematic and quantitative discussion of the basic working principles of the attacks, also in terms of the inner smart grid vulnerabilities and dynamical properties exploited by the attack. The main contribution of the paper is the attempt to provide a unifying view, highlighting the fundamental aspects and the common working principles shared by the attack models, even when targeting different subsystems of the smart grid.

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

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