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DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

Ferrag, Mohamed Amine; Maglaras, Leandros


Mohamed Amine Ferrag


In this paper, we propose a novel deep learning and blockchain-based energy framework for smart grids, entitled DeepCoin. The DeepCoin framework uses two schemes, a blockchain-based scheme and a deep learning-based scheme. The blockchain-based scheme consists of five phases: setup phase, agreement phase, creating a block phase and consensus-making phase, and view change phase. It incorporates a novel reliable peer-to-peer energy system that is based on the practical Byzantine fault tolerance algorithm and it achieves high throughput. In order to prevent smart grid attacks, the proposed framework makes the generation of blocks using short signatures and hash functions. The proposed deep learning-based scheme is an intrusion detection system (IDS), which employs recurrent neural networks for detecting network attacks and fraudulent transactions in the blockchain-based energy network. We study the performance of the proposed IDS on three different sources the CICIDS2017 dataset, a power system dataset, and a web robot (Bot)-Internet of Things (IoT) dataset.

Journal Article Type Article
Online Publication Date Jul 9, 2019
Publication Date 2020-11
Deposit Date Dec 5, 2022
Journal IEEE Transactions on Engineering Management
Print ISSN 0018-9391
Electronic ISSN 1558-0040
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 67
Issue 4
Pages 1285-1297
Keywords Blockchain, intrusion detection system (IDS), machine learning, smart grid, security
Public URL