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- Publisher Website: 10.1109/TETCI.2019.2902845
- Scopus: eid_2-s2.0-85085867839
- WOS: WOS:000682799900006
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Article: Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based Approach
Title | Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based Approach |
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Authors | |
Keywords | and false data injection delayed feedback reservoir recurrent neural network Spiking neural networks |
Issue Date | 2020 |
Citation | IEEE Transactions on Emerging Topics in Computational Intelligence, 2020, v. 4, n. 3, p. 253-264 How to Cite? |
Abstract | Spiking neural networks have been widely used for supervised pattern recognition exploring the underlying spatio-temporal correlation. Meanwhile, spatio-temporal correlation manifests significantly between different components in a smart grid making the spiking neural network a desirable candidate for false data injection attack detection. In this paper, we develop a spiking-neural-network-based technique for dynamic cyber-attack detection in a smart grid. This is achieved through judiciously integrating spiking neurons with a special recurrent neural network called the delayed feedback reservoir computing. The inter-spike interval encoding is also explored in the precise-spike-driven synaptic plasticity based training process. The simulation results suggest that the introduced method outperforms multi-layer perceptrons and can achieve a significantly better performance compared to the state-of-the-art techniques. Furthermore, our analysis indicates that the delay value in the delayed feedback reservoir will have a substantial impact on the overall system performance. |
Persistent Identifier | http://hdl.handle.net/10722/336236 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hamedani, Kian | - |
dc.contributor.author | Liu, Lingjia | - |
dc.contributor.author | Hu, Shiyan | - |
dc.contributor.author | Ashdown, Jonathan | - |
dc.contributor.author | Wu, Jinsong | - |
dc.contributor.author | Yi, Yang | - |
dc.date.accessioned | 2024-01-15T08:24:44Z | - |
dc.date.available | 2024-01-15T08:24:44Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Emerging Topics in Computational Intelligence, 2020, v. 4, n. 3, p. 253-264 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336236 | - |
dc.description.abstract | Spiking neural networks have been widely used for supervised pattern recognition exploring the underlying spatio-temporal correlation. Meanwhile, spatio-temporal correlation manifests significantly between different components in a smart grid making the spiking neural network a desirable candidate for false data injection attack detection. In this paper, we develop a spiking-neural-network-based technique for dynamic cyber-attack detection in a smart grid. This is achieved through judiciously integrating spiking neurons with a special recurrent neural network called the delayed feedback reservoir computing. The inter-spike interval encoding is also explored in the precise-spike-driven synaptic plasticity based training process. The simulation results suggest that the introduced method outperforms multi-layer perceptrons and can achieve a significantly better performance compared to the state-of-the-art techniques. Furthermore, our analysis indicates that the delay value in the delayed feedback reservoir will have a substantial impact on the overall system performance. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Emerging Topics in Computational Intelligence | - |
dc.subject | and false data injection | - |
dc.subject | delayed feedback reservoir | - |
dc.subject | recurrent neural network | - |
dc.subject | Spiking neural networks | - |
dc.title | Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based Approach | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TETCI.2019.2902845 | - |
dc.identifier.scopus | eid_2-s2.0-85085867839 | - |
dc.identifier.volume | 4 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 253 | - |
dc.identifier.epage | 264 | - |
dc.identifier.eissn | 2471-285X | - |
dc.identifier.isi | WOS:000682799900006 | - |