scholarly journals A Trust-Based Framework and Deep Learning-Based Attack Detection for Smart Grid Home Area Network

2014 ◽  
Vol 12 (8) ◽  
pp. 3778-3785
Author(s):  
Halim Halimi ◽  
Aristotel Tentov

Securing the network communication for the smart grid, it is important that a secure key distribution and management scheme is needed. Advanced metering Infrastructure (AMI) is considered as integral part of the smart grid that collects and analyzes the information of energy consumption using the communication network. In order to achieve data confidentiality, privacy, and authentication in AMI, various crypto algorithms are used, such as demand key distribution and management schemes. Investigation on properly designed key distribution and management scheme for securely gathering both individual and aggregated meter’s readings is one of the critical requirements. In this paper, we propose a secure key distribution for Home Area Network (HAN) in smart grid. In this paper, we propose a new key distribution and management scheme, which uses identification scheme of Wu – Hsu and tailored to smart grid. We also give the scheme for key update/freshness and key revocation. Specifically, a group ID based mechanism is proposed to establish the keys for a large amount of users with small overload.


2021 ◽  
pp. 49-63
Author(s):  
Piyush Kumar Shukla ◽  
◽  
Prashant Kumar Shukla ◽  

Gateway based Home Area Network (HAN ) to Neighbourhood Area Network (NAN); NAN to HAN improved the communication in the Smart grid. The gateway reduces the Load dispatch centre (LDC) work in varying power consumption in a short time interval. The proposed work explains the working of gateway, reducing the work of LDC using the load scheduling procedure. Deep learning methods incorporated gateway will aid in achieving the requirement. It reduces black start operation and it may be prevented by indulging consumers in the supply automation of the grid. It will produce the grid to maintain the operating frequency, avoiding the substations' disciplinary charges. A variety of types of abrupt load variation and load kinds has been taken in this function. The analysis shows that the gateway achieves a decrease in complexity in the proposed work. This method provides the detail of employment of deep learning for predicting the load forecasting performances in smart grids that can be made better through a gateway between SMs-DCU. The Proposed work compares with systems that employ the predictable profound estimating methods for load forecasting, which has provided better performance. Load Prediction, Deep Learning, Gateway, Smart Grid, Estimation


2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Sergio Saponara ◽  
Tony Bacchillone

This paper discusses aims, architecture, and security issues of Smart Grid, taking care of the lesson learned at University of Pisa in research projects on smart energy and grid. A key element of Smart Grid is the energy home area network (HAN), for which an implementation is proposed, dealing with its security aspects and showing some solutions for realizing a wireless network based on ZigBee. Possible hardware-software architectures and implementations using COTS (Commercial Off The Shelf) components are presented for key building blocks of the energy HAN such as smart power meters and plugs and a home smart information box providing energy management policy and supporting user's energy awareness.


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