Smart Grid Data Security Sharing Mechanism Based on Alliance Blockchain

2021 ◽  
pp. 784-792
Author(s):  
Zhijian Si ◽  
Dahai Xiao ◽  
Chao Yang ◽  
Xiaolei Tian ◽  
Zhenjiang Lei ◽  
...  
Keyword(s):  
Smart Grid ◽  
Data Security ◽  
Grid Data

scholarly journals ENSURING SMART GRID DATA SECURITY AT CLOUD DATA CENTRES

2015 ◽  
Author(s):  
E.Chaitanya Krishna ◽  
K. Venkataramana ◽  
Sulaiman AlMuhteb
Keyword(s):  
Smart Grid ◽  
Data Security ◽  
Grid Data ◽  
Cloud Data ◽  
Data Centres

scholarly journals The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3731 ◽  
Author(s):  
Mohamad El Hariri ◽  
Eric Harmon ◽  
Tarek Youssef ◽  
Mahmoud Saleh ◽  
Hany Habib ◽  
...  
Keyword(s):  
Smart Grid ◽  
Protocol Analysis ◽  
Data Detection ◽  
Detection Accuracy ◽  
Wide Spread ◽  
Grid Data ◽  
Iec 61850 ◽  
Forecasting Error ◽  
Heterogeneous Nature ◽  
Statistical Indicators

The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.

Smart Grid Data Integrity Attacks

2013 ◽  
Vol 4 (3) ◽  
pp. 1244-1253 ◽  
Author(s):  
Annarita Giani ◽  
Eilyan Bitar ◽  
Manuel Garcia ◽  
Miles McQueen ◽  
Pramod Khargonekar ◽  
...  
Keyword(s):  
Smart Grid ◽  
Data Integrity ◽  
Grid Data

Energy Big Data Security Threats in IoT-Based Smart Grid Communications

2017 ◽  
Vol 55 (10) ◽  
pp. 70-75 ◽  
Author(s):  
Wen-Long Chin ◽  
Wan Li ◽  
Hsiao-Hwa Chen
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Data Security ◽  
Security Threats ◽  
Smart Grid Communications

Security Analysis of Smart Grid Data Collection Technologies

2011 ◽  
pp. 143-156 ◽  
Author(s):  
Luigi Coppolino ◽  
Salvatore D’Antonio ◽  
Ivano Alessandro Elia ◽  
Luigi Romano
Keyword(s):  
Data Collection ◽  
Smart Grid ◽  
Security Analysis ◽  
Grid Data
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