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 Cognitive Dynamic System for AC State Estimation and Cyber-Attack Detection in Smart Grid

2020 ◽  
Author(s):  
Mohammad Irshaad Oozeer ◽  
Simon Haykin
Keyword(s):  
Smart Grid ◽  
Dynamic System ◽  
State Estimation ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Data Detection ◽  
Cyber Attack ◽  
Bad Data Detection ◽  
Cycle Basis ◽  
Iterative Procedures

The work presented in this chapter is an extension of our previous research of bringing together the Cognitive Dynamic System (CDS) and the Smart Grid (SG) by focusing on AC state estimation and Cyber-Attack detection. Under the AC power flow model, state estimation is complex and computationally expensive as it relies on iterative procedures. On the other hand, the False Data Injection (FDI) attacks are a new category of cyber-attacks targeting the SG that can bypass the current bad data detection techniques in the SG. Due to the complexity of the nonlinear system involved, the amount of published works on AC based FDI attacks have been fewer compared to their DC counterpart. Here, we will demonstrate how the entropic state, which is the objective function of the CDS, can be used as a metric to monitor the grid’s health and detect FDI attacks. The CDS, acting as the supervisor of the system, improves the entropic state on a cycle to cycle basis by dynamically optimizing the state estimation process through the reconfiguration of the weights of the sensors in the network. In order to showcase performance of this new structure, computer simulations are carried out on the IEEE 14-bus system for optimal state estimation and FDI attack detection.

scholarly journals IoT based Smart Grid Attack Resistance in AC and DC State Estimation

2021 ◽  
Vol 2 (3) ◽  
pp. 118-122
Author(s):  
Vivekanadam B
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Smart Grid ◽  
Error Detection ◽  
Smart Grids ◽  
Data Detection ◽  
Bad Data Detection ◽  
Security Issues ◽  
Number Of Factors ◽  
Bad Data

Use of automation and intelligence in smart grids has led to implementation in a number of applications. When internet of things is incorporated it will result in the significant improvement a number of factors such as fault recovery, energy delivery efficiency, demand response and reliability. However, the collaboration of internet of things and smart grid gives rise to a number of security issues and threats. This is especially the case when using internet based protocols and public communication infrastructure. To address these issues we should ensure that the data stored is secure and critical information from the data is extracted in a careful manner. If any threat to its security is detective an early blackout warning should be issued immediately. In this paper we have proposed a geometric view point for big data attacks which is capable of bypassing bad data detection. We have created an environment where replay scheme is used launch blind energy big data attack. The defence mechanism of our proposed work is studied and found to be efficient. Experimental evidence supports our theory and we have found our methodology to efficiently improve error detection rate.

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 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 PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Levent Yavuz ◽  
Ahmet Soran ◽  
Ahmet Onen ◽  
SM Muyeen
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Detection Algorithm ◽  
Cyber Attacks ◽  
Data Detection ◽  
Particle Swarm Optimizer ◽  
Bad Data Detection ◽  
Detection Mechanisms ◽  
Bad Data ◽  
Advanced Computer

Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.

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.

Malicious Node Detection in Smart Grid Networks

2021 ◽  
Author(s):  
Faisal Y Al Yahmadi ◽  
Muhammad R Ahmed
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Electricity Consumption ◽  
Cyber Attacks ◽  
Support Vector ◽  
Smart Meters ◽  
Malicious Nodes ◽  
High Detection Rate ◽  
Detection Model ◽  
Electricity Theft

Many countries around the world are implementing smart grids and smart meters. Malicious users that have moderate level of computer knowledge can manipulate smart meters and launch cyber-attacks. This poses cyber threats to network operators and government security. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we propose a model based on software that detects malicious nodes in a smart grid network. The model collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) model is implemented to classify nodes into good or malicious nodes by (high dimensional) giving the statues of 1 for good nodes and status of -1 for malicious (abnormal) nodes. The detection model also displays the network graphically as well as the data table. Moreover, this model displays the detection error in each cycle. It has a very low false alarm rate (2%) and a high detection rate as high as (98%). Future developments can trace the attack origin to eliminate or block the attack source minimizing losses before human control arrives.

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