SCA-LFD: Side-Channel Analysis based Load Forecasting Disturbance in the Energy Internet

Li Ding, Jun Wu, Changlian Li, Alireza Jolfaei, Xi Zheng

Research output: Contribution to journalArticlepeer-review

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 paper 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.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Industrial Electronics
DOIs
Publication statusAccepted/In press - 2022
Externally publishedYes

Keywords

  • Analytical models
  • Collaborative work
  • Data models
  • Energy Internet
  • Federated Learning
  • Internet
  • Load forecasting
  • Load modeling
  • Predictive models
  • Side-Channel Analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'SCA-LFD: Side-Channel Analysis based Load Forecasting Disturbance in the Energy Internet'. Together they form a unique fingerprint.

Cite this