scholarly journals Security and Privacy in Smart Grid

2018 ◽  
Author(s):  
Asmaa Abdallah ◽  
Xuemin Shen
Keyword(s):  
Smart Grid ◽  
Security And Privacy

Anonymous Authentication for Smart Grid Communications

2011 ◽  
Vol 145 ◽  
pp. 364-368 ◽  
Author(s):  
Tung Hung Chueh ◽  
Huei Ru Tseng
Keyword(s):  
Smart Grid ◽  
Distribution Networks ◽  
Research Literature ◽  
Security Requirements ◽  
Security And Privacy ◽  
User Privacy ◽  
Smart Meters ◽  
Anonymous Authentication ◽  
Smart Grid Communications ◽  
Smart Grid Communication

The smart grid is a network of computers and power infrastructures that monitor and manage energy usage and uses intelligent transmission and distribution networks to deliver electricity for improving the electric system’s reliability and efficiency. With grid controls, energy transmission management could be enhanced and resilience to control-system failures would be increased. Processing chips and storage units have been embedded into traditional electricity meters, so that they are capable of performing smart functions, called smart meters. Then, smart meters communicate with electrical appliances at home as well as the generation and management facilities at the power companies. Although deploying the smart grid has numerous social and technical benefits, several security and privacy concerns arise. Attackers might compromise smart meters, eavesdrop the communication, or hack into the power company’s database, to access power consumption data of the victim, from which they learn about the victim’s daily activities. Recently, various security and privacy vulnerabilities and threats have been studied in the research literature, however, most of the problems remain yet to be addressed. Therefore, it is crucial to design secure smart grid communication protocols that could prevent all possible security vulnerabilities. In this paper, we propose an anonymous authentication protocol for securing communication among various smart meters of the smart grid. The proposed protocol can achieve key agreement between smart meters and fully protect user privacy with low computation overhead. In addition, the analysis shows that the proposed protocol can satisfy the desirable security requirements and resist several notorious attacks.

scholarly journals Continuous Quantitative Risk Management in Smart Grids Using Attack Defense Trees

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4404 ◽  
Author(s):  
Erkuden Rios ◽  
Angel Rego ◽  
Eider Iturbe ◽  
Marivi Higuero ◽  
Xabier Larrucea
Keyword(s):  
Risk Assessment ◽  
Smart Grid ◽  
Smart Grids ◽  
Investment Decision ◽  
Holistic Approach ◽  
Security And Privacy ◽  
Sensitivity Analyses ◽  
Building Energy Efficiency ◽  
Risk Minimization ◽  
Grid Systems

Although the risk assessment discipline has been studied from long ago as a means to support security investment decision-making, no holistic approach exists to continuously and quantitatively analyze cyber risks in scenarios where attacks and defenses may target different parts of Internet of Things (IoT)-based smart grid systems. In this paper, we propose a comprehensive methodology that enables informed decisions on security protection for smart grid systems by the continuous assessment of cyber risks. The solution is based on the use of attack defense trees modelled on the system and computation of the proposed risk attributes that enables an assessment of the system risks by propagating the risk attributes in the tree nodes. The method allows system risk sensitivity analyses to be performed with respect to different attack and defense scenarios, and optimizes security strategies with respect to risk minimization. The methodology proposes the use of standard security and privacy defense taxonomies from internationally recognized security control families, such as the NIST SP 800-53, which facilitates security certifications. Finally, the paper describes the validation of the methodology carried out in a real smart building energy efficiency application that combines multiple components deployed in cloud and IoT resources. The scenario demonstrates the feasibility of the method to not only perform initial quantitative estimations of system risks but also to continuously keep the risk assessment up to date according to the system conditions during operation.

scholarly journals Lightweight Security and Privacy Safeguard Strategies for Smart Grid Management in Client-side Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 917-923
Keyword(s):  
Smart Grid ◽  
Communication Networks ◽  
Power Grid ◽  
Electricity Consumption ◽  
Local Area ◽  
Security And Privacy ◽  
Intelligent Network ◽  
Communication Architectures ◽  
The Status ◽  
Client Side

In order to improve the reliability and efficiency of the power grid, the smart grid uses different communication technologies. Smart grid allows bidirectional flow of electricity and information, about the state of the network and the preconditions of the clients, between the different parts of the network. Therefore, it reduces energy losses and generates and distributes electricity efficiently. Although smart grid improves the quality of network services, due to the nature of the power grid communication networks are exposed to cybersecurity threats along with the other threats. For example, electricity consumption messages sent by consumers to the utility through the wireless network can be captured, modified or reproduced by adversaries. As a consequence, the important challenges in smart grid seems to be security and privacy concerns. The smart grid update creates three main communication architectures: the first is communication between the utility companies and customers through diverse networks; that is, Local Area Networks (HAN), Construction Area Networks (BAN) and Neighboring Area Networks (NAN), we refer to these networks as client-side networks in our thesis. The second architecture is the communication through the vehicle-to-network (V2G) connection between the Electric Vehicles and the network to charge or discharge their batteries. The hindmost network is connection of the network with measurement units that extend throughout the network in order to monitor the status and send reports periodically to the main CC to estimate the status and detect erroneous data. The proposed schemes are promising solutions for the security and privacy problems of the three main communication networks in smart grid. The novelty of these proposed schemes is not only because they are robust and efficient security solutions, but also due to their lightweight communication and computing overhead, which qualifies them to be applicable in devices with limited capacity in the network. Therefore, this work is considered an important progress towards a more reliable and authentic intelligent network.

scholarly journals Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 130 ◽  
Author(s):  
Mohammad Navid Fekri ◽  
Ananda Mohon Ghosh ◽  
Katarina Grolinger
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Energy Consumption ◽  
Smart Grid ◽  
Time Series Data ◽  
Security And Privacy ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data, Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models.

Trust Management Issues for Sensors Security and Privacy in the Smart Grid

2018 ◽  
pp. 86-103
Author(s):  
Nawal Ait Aali ◽  
Amine Baina ◽  
Loubna Echabbi
Keyword(s):  
Renewable Energy ◽  
Smart Grid ◽  
Real Time ◽  
Smart Grids ◽  
Trust Management ◽  
Electrical Energy ◽  
Security And Privacy ◽  
Electric Networks ◽  
Electrical Grid ◽  
The World

Currently, smart grids have changed the world, given the great benefits of these critical infrastructures regarding the customers' satisfaction by offering them the electrical energy that they need for their business. Also, the smart grid aims to solve all the problems encountered in the current electrical grid (outage, lack of renewable energy, an excess in the produced power, etc.) by transmitting and sharing the information in real time between the different entities through the installation of the sensors. This chapter therefore presents the architecture of the smart grid by describing its objectives and advantages. In addition, the microgrids are presented as small electric networks. Then, focusing on the security aspects, an analysis of the different attacks and risks faced in the smart grids and more particularly in the microgrids is presented. After, different techniques and suitable security solutions are detailed to protect and secure the various elements of the smart grids and microgrids.

Sign in / Sign up

Export Citation Format

Share Document