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

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 p...

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Veröffentlicht in:IEEE transactions on engineering management Jg. 67; H. 4; S. 1285 - 1297
Hauptverfasser: Ferrag, Mohamed Amine, Maglaras, Leandros
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Blockchain
Computational modeling
Cryptography
Datasets
Deep learning
Fault tolerance
Hash based algorithms
Internet of Things
Intrusion detection
intrusion detection system (IDS)
Machine learning
Peer-to-peer computing
Privacy
Recurrent neural networks
security
Smart grid
Smart grids
ISSN:0018-9391, 1558-0040
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2019.2922936