Big Data And Cyber Security Issues In Smart Grids

2021 ◽  
Author(s):  
Seref Sagiroglu
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Smart Grids ◽  
Security Issues

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 Evaluation of big data frameworks for analysis of smart grids

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Hasan Ansari ◽  
Vahid Tabatab Vakili ◽  
Behnam Bahrak
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Cyber Security ◽  
Smart Grids ◽  
Large Scale ◽  
Rapid Development ◽  
Massive Data ◽  
Grid Data ◽  
Data Generator ◽  
And Performance

AbstractWith the rapid development of smart grids and increasing data collected in these networks, analyzing this massive data for applications such as marketing, cyber-security, and performance analysis, has gained popularity. This paper focuses on analysis and performance evaluation of big data frameworks that are proposed for handling smart grid data. Since obtaining large amounts of smart grid data is difficult due to privacy concerns, we propose and implement a large scale smart grid data generator to produce massive data under conditions similar to those in real smart grids. We use four open source big data frameworks namely Hadoop-Hbase, Cassandra, Elasticsearch, and MongoDB, in our implementation. Finally, we evaluate the performance of different frameworks on smart grid big data and present a performance benchmark that includes common data analysis techniques on smart grid data.

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.

Cloud-Based Dynamic Line Rating

2022 ◽  
pp. 488-505
Author(s):  
Valentina V. Timčenko
Keyword(s):  
Cyber Security ◽  
Smart Grids ◽  
Operating Conditions ◽  
Cloud Services ◽  
Security And Privacy ◽  
Real Time Control ◽  
Public And Private ◽  
Overhead Transmission Line ◽  
Security Issues ◽  
Time Control

Introduction of the Dynamic Line Rating (DLR) concept has an important role in implementing smart grids in the power utility's transmission network. DLR assumes real-time control of the overhead transmission line, based on the continuous evaluation of the actual thermal and other operating conditions, and further estimation of the maximum transmission line's load and other relevant parameters that determine operational limitations. This chapter presents cloud-based DLR systems in terms of architecture, cloud services, and cyber security issues. DLR systems are explored with regards to cloud computing in industry, applicable cloud services and infrastructures, and communication system's performance. Security and privacy of cloud-based DLR systems have been addressed in terms of public and private services. A secure hybrid cloud-based architecture to support DLR is proposed.

Reliability of Smart Grid Including Cyber Impact

2021 ◽  
pp. 163-174
Author(s):  
Janavi Popat ◽  
Harsh Kakadiya ◽  
Lalit Tak ◽  
Neeraj Kumar Singh ◽  
Mahshooq Abdul Majeed ◽  
...  
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Cyber Security ◽  
Smart Grids ◽  
Reliability Assessment ◽  
Grid Security ◽  
Security Issues ◽  
Smart Grid Security ◽  
Intelligent Electronic Devices ◽  
Improve Reliability

Smart grid has changed power systems and their reliability concerns. Along with that, cyber security issues are also introduced due to the use of intelligent electronic devices (IEDs), wireless sensory network (WSN), and internet of things (IoT) for two-way communication. This chapter presents a review of different methods used from 2010 to 2020 focusing on citation as the main criteria for reliability assessment of smart grids and proposals to improve reliability when it comes to assessing a practical transmission system. It shows that evolutionary techniques are the latest trend for smart grid security.

Deep learning algorithms for cyber security applications: A survey

2021 ◽  
pp. 1-25
Author(s):  
Guangjun Li ◽  
Preetpal Sharma ◽  
Lei Pan ◽  
Sutharshan Rajasegarar ◽  
Chandan Karmakar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Big Data ◽  
Deep Learning ◽  
Cyber Security ◽  
High Dimensional ◽  
The Internet ◽  
Security Threats ◽  
Security Issues ◽  
Security Applications

With the development of information technology, thousands of devices are connected to the Internet, various types of data are accessed and transmitted through the network, which pose huge security threats while bringing convenience to people. In order to deal with security issues, many effective solutions have been given based on traditional machine learning. However, due to the characteristics of big data in cyber security, there exists a bottleneck for methods of traditional machine learning in improving security. Owning to the advantages of processing big data and high-dimensional data, new solutions for cyber security are provided based on deep learning. In this paper, the applications of deep learning are classified, analyzed and summarized in the field of cyber security, and the applications are compared between deep learning and traditional machine learning in the security field. The challenges and problems faced by deep learning in cyber security are analyzed and presented. The findings illustrate that deep learning has a better effect on some aspects of cyber security and should be considered as the first option.

Cloud-Based Dynamic Line Rating

2020 ◽  
pp. 295-312
Author(s):  
Valentina V. Timčenko
Keyword(s):  
Cyber Security ◽  
Smart Grids ◽  
Operating Conditions ◽  
Cloud Services ◽  
Security And Privacy ◽  
Real Time Control ◽  
Public And Private ◽  
Overhead Transmission Line ◽  
Security Issues ◽  
Time Control

Introduction of the Dynamic Line Rating (DLR) concept has an important role in implementing smart grids in the power utility's transmission network. DLR assumes real-time control of the overhead transmission line, based on the continuous evaluation of the actual thermal and other operating conditions, and further estimation of the maximum transmission line's load and other relevant parameters that determine operational limitations. This chapter presents cloud-based DLR systems in terms of architecture, cloud services, and cyber security issues. DLR systems are explored with regards to cloud computing in industry, applicable cloud services and infrastructures, and communication system's performance. Security and privacy of cloud-based DLR systems have been addressed in terms of public and private services. A secure hybrid cloud-based architecture to support DLR is proposed.

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