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.

IoT Big Data Security, Privacy and Challenges Related to Smart Grid

2022 ◽  
pp. 368-379
Author(s):  
Kimmi Kumari ◽  
M. Mrunalini
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Smart Grids ◽  
Type Systems ◽  
Heterogeneous Data ◽  
Security Measures ◽  
Smart Meters ◽  
Efficient Manner ◽  
Security Issues ◽  
Iot Devices

The highly interconnected network of heterogeneous devices which enables all kinds of communications to take place in an efficient manner is referred to as “IOT.” In the current situation, the data are increasing day by day in size as well as in terms of complexities. These are the big data which are in huge demand in the industrial sectors. Various IT sectors are adopting big data present on IOT for the growth of their companies and fulfilling their requirements. But organizations are facing a lot of security issues and challenges while protecting their confidential data. IOT type systems require security while communications which is required currently by configuration levels of security algorithms, but these algorithms give more priority to functionalities of the applications over security. Smart grids have become one of the major subjects of discussions when the demands for IOT devices increases. The requirements arise related to the generation and transmission of electricity, consumption of electricity being monitored, etc. The system which is responsible to collect heterogeneous data are a complicated structure and some of its major subsystems which they require for smooth communications include log servers, smart meters, appliances which are intelligent, different sensors chosen based on their requirements, actuators with proper and efficient infrastructure. Security measures like collection, storage, manipulations and a massive amount of data retention are required as the system is highly diverse in its architecture and even the heterogeneous IOT devices are interacting with each other. In this article, security challenges and concerns of IOT big data associated with smart grid are discussed along with the new security enhancements for identification and authentications of things in IOT big data environments.

Bad data detection in the smart grid

2017 ◽  
pp. 151-163
Author(s):  
Haris M. Khalid Khalid ◽  
Ahmed Al-Durra Al-Durra
Keyword(s):  
Smart Grid ◽  
Data Detection ◽  
Bad Data Detection ◽  
Bad Data

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.

Bad-data detection in smart grid: a distributed approach

2013 ◽  
pp. 175-190 ◽  
Author(s):  
Le Xie ◽  
Dae-Hyun Choi ◽  
Soummya Kar ◽  
H. Vincent Poor
Keyword(s):  
Smart Grid ◽  
Data Detection ◽  
Bad Data Detection ◽  
Distributed Approach ◽  
Bad Data
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