| Title | DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Ferrag, Mohamed Amine, Maglaras, Leandros |
| Journal | IEEE Transactions on Engineering Management |
| Volume | 67 |
| Pagination | 1285–1297 |
| Date Published | nov |
| ISSN | 1558-0040 |
| Keywords | blockchain, composability, Computational modeling, Deep Learning, Intrusion detection, Intrusion Detection System (IDS), intrusion tolerance, machine learning, Peer-to-peer computing, privacy, pubcrawl, Resiliency, security, Smart grid, Smart grids |
| Abstract | 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. |
| DOI | 10.1109/TEM.2019.2922936 |
| Citation Key | ferrag_deepcoin_2020 |
DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids