# Deep Reinforcement Learning for DER Cyber-Attack Mitigation

arXiv: https://arxiv.org/abs/2009.13088 · HTML: https://mlsanity.com/paper/2009.13088/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Automated Cyber Defence: A Review](https://mlsanity.com/paper/2303.04926/) (2023)
- [Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids](https://mlsanity.com/paper/2209.12693/) (2022)
- [Real-Time Defense Against Coordinated Cyber-Physical Attacks: A Robust Constrained Reinforcement Learning Approach](https://mlsanity.com/paper/2509.10999/) (2025)
- [Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants](https://mlsanity.com/paper/2610.15183/) (2026)

## Abstract (arXiv)

The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption, to an active network with hundreds of distributed controllers dynamically modulating their operating setpoints as a function of system conditions. This transition is being achieved through standardization of functionality through grid codes and/or international standards. DER, however, are unique in that they are typically neither owned nor operated by distribution utilities and, therefore, represent a new emerging attack vector for cyber-physical attacks. Within this work we consider deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.