# Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks

arXiv: https://arxiv.org/abs/2104.02774 · HTML: https://mlsanity.com/paper/2104.02774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 20 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [(More) Efficient Reinforcement Learning via Posterior Sampling](https://mlsanity.com/paper/1306.0940/) (2013)
- [Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with Non-stationary Rewards](https://mlsanity.com/paper/1405.3316/) (2014)
- [The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey](https://mlsanity.com/paper/1502.00134/) (2015)
- [Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks](https://mlsanity.com/paper/1609.02174/) (2016)

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

- [Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context](https://mlsanity.com/paper/2404.13391/) (2024)

## Abstract (arXiv)

The cybersecurity of smart grids has become one of key problems in developing reliable modern power and energy systems. This paper introduces a non-stationary adversarial cost with a variation constraint for smart grids and enables us to investigate the problem of optimal smart grid protection against cyber attacks in a relatively practical scenario. In particular, a Bayesian multi-node bandit (MNB) model with adversarial costs is constructed and a new regret function is defined for this model. An algorithm called Thompson-Hedge algorithm is presented to solve the problem and the superior performance of the proposed algorithm is proven in terms of the convergence rate of the regret function. The applicability of the algorithm to real smart grid scenarios is verified and the performance of the algorithm is also demonstrated by numerical examples.