- Title
- SCA-LFD : side-channel analysis-based load forecasting disturbance in the energy internet
- Creator
- Ding, Li; Wu, Jun; Li, Changlian; Jolfaei, Alireza; Zheng, Xi
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189091
- Identifier
- vital:17366
- Identifier
-
https://doi.org/10.1109/TIE.2022.3170641
- Identifier
- ISSN:0278-0046 (ISSN)
- Abstract
- The energy Internet (EI) equipment may face threats that attackers poison federated learning (FL) models to disturb electricity load forecasting. To mitigate this vulnerability, it is important to study load forecasting disturbance approaches. This article proposes a side-channel analysis (SCA)-based disturbance approach. First, we design an FL SCA scheme to extract power information from the FL chip running forecasting model. Second, we propose an FL data speculation method using an optimized convolutional neural network trained with SCA information. Third, we design a label-flipping-based poisoning scheme with speculated data characteristics for load forecasting disturbance. Experimental results show attackers can successfully poison and disturb FL-based load forecasting. The average accuracy of EI load data speculation is 99.8%. This work is the first to study EI load forecasting disturbance from an SCA perspective. © 1982-2012 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Industrial Electronics Vol. 70, no. 3 (2023), p. 3199-3208
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2022 IEEE
- Subject
- 40 Engineering; 46 Information and computing sciences; Energy Internet (EI); Federated learning (FL); Side-channel analysis (SCA)
- Reviewed
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