# Causative Cyberattacks on Online Learning-based Automated Demand Response Systems

arXiv: https://arxiv.org/abs/2307.15175 · HTML: https://mlsanity.com/paper/2307.15175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Certified Defenses for Data Poisoning Attacks](https://mlsanity.com/paper/1706.03691/) (2017)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Iterative Machine Teaching](https://mlsanity.com/paper/1705.10470/) (2017)
- [Risk-Sensitive Learning and Pricing for Demand Response](https://mlsanity.com/paper/1611.07098/) (2016)
- [A Distributed Online Pricing Strategy for Demand Response Programs](https://mlsanity.com/paper/1702.05551/) (2017)
- [Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?](https://mlsanity.com/paper/1907.08283/) (2019)
- [Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints](https://mlsanity.com/paper/1908.07964/) (2019)
- [A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications](https://mlsanity.com/paper/2005.02339/) (2020)
- [Online Learning for Network Constrained Demand Response Pricing in Distribution Systems](https://mlsanity.com/paper/1811.09384/) (2018)

## Built on by (most cited first, 1 of 1)

- [Automated Security Response through Online Learning with Adaptive Conjectures](https://mlsanity.com/paper/2402.12499/) (2024)

## Abstract (arXiv)

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 textitcausative 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.