# Learning to Attack Powergrids with DERs

arXiv: https://arxiv.org/abs/2204.11352 · HTML: https://mlsanity.com/paper/2204.11352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 24 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks](https://mlsanity.com/paper/1904.06606/) (2019)
- [Fast Local Voltage Control under Limited Reactive Power: Optimality and Stability Analysis](https://mlsanity.com/paper/1510.07013/) (2015)
- [Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems](https://mlsanity.com/paper/1811.06447/) (2018)
- [Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence](https://mlsanity.com/paper/2006.06074/) (2020)
- [Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence](https://mlsanity.com/paper/1908.11779/) (2019)

## Abstract (arXiv)

In the past years, power grids have become a valuable target for cyber-attacks. Especially the attacks on the Ukrainian power grid has sparked numerous research into possible attack vectors, their extent, and possible mitigations. However, many fail to consider realistic scenarios in which time series are incorporated into simulations to reflect the transient behaviour of independent generators and consumers. Moreover, very few consider the limited sensory input of a potential attacker. In this paper, we describe a reactive power attack based on a well-understood scenario. We show that independent agents can learn to use the dynamics of the power grid against it and that the attack works even in the face of other generator and consumer nodes acting independently.