Causative Cyberattacks on Online Learning-Based Automated Demand Response Systems

Samrat Acharya, Yury Dvorkin, Ramesh Karri

Research output: Contribution to journalArticlepeer-review


Power utilities are adopting Automated Demand Response (ADR) to replace the costly fuel-fired generators and to preempt congestion during peak electricity demand. Similarly, third-party Demand Response (DR) aggregators are leveraging controllable small-scale electrical loads to provide on-demand grid support services to the utilities. Some aggregators and utilities have started employing Artificial Intelligence (AI) to learn the energy usage patterns of electricity consumers and use this knowledge to design optimal DR incentives. Such AI frameworks use open communication channels between the utility/aggregator and the DR customers, which are vulnerable to causative data integrity cyberattacks. This paper explores vulnerabilities of AI-based DR learning and designs a data-driven attack strategy informed by DR data collected from the New York University (NYU) campus buildings. The case study demonstrates the feasibility and effects of maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent to DR customers, and (iii) responses of DR customers to the DR incentives.

Original languageEnglish (US)
Article number9383108
Pages (from-to)3548-3559
Number of pages12
JournalIEEE Transactions on Smart Grid
Issue number4
StatePublished - Jul 2021


  • Artificial intelligence
  • Causative attacks
  • Computer crime
  • cybersecurity
  • demand response
  • Load management
  • Mathematical model
  • Power grids
  • Protocols
  • shapley value
  • smart grids.
  • Training data
  • smart grids

ASJC Scopus subject areas

  • General Computer Science


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