# Stochastic defense against complex grid attacks

arXiv: https://arxiv.org/abs/1807.06707 · HTML: https://mlsanity.com/paper/1807.06707/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

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

- [Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment](https://mlsanity.com/paper/2504.14412/) (2025)

## Abstract (arXiv)

We describe defense mechanisms designed to detect sophisticated grid attacks involving both physical actions (including load modification) and sensor output alteration, with the latter performed in a sparse manner and also so as to take into account grid response mechanisms (secondary response). The attacks aim to be both undetectable even under a full AC power flow model, and to hide equipment overload. We demonstrate that such attacks, while perhaps difficult to implement, nevertheless are easily computed even on systems with a large number of installed sensors, and can, in a static setting, successfuly hide large line overloads. Furthermore an attacker that understands the ongoing stochastic nature of sensor signals can extend the attack so as to remain effective for a nontrivial time period. To counteract such "ideal" or "perfect" attacks, we demonstrate a set of data-driven stochastic techniques to thwart the attacker and detect that an attack has taken place.