# Towards Type Agnostic Cyber Defense Agents

arXiv: https://arxiv.org/abs/2412.01542 · HTML: https://mlsanity.com/paper/2412.01542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [Sub-policy Adaptation for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1906.05862/) (2019)
- [A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems](https://mlsanity.com/paper/1506.01170/) (2015)
- [Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation](https://mlsanity.com/paper/2207.12355/) (2022)
- [Simulation of Attacker Defender Interaction in a Noisy Security Game](https://mlsanity.com/paper/2212.04281/) (2022)
- [The Price of Pessimism for Automated Defense](https://mlsanity.com/paper/2409.19237/) (2024)

## Abstract (arXiv)

With computing now ubiquitous across government, industry, and education, cybersecurity has become a critical component for every organization on the planet. Due to this ubiquity of computing, cyber threats have continued to grow year over year, leading to labor shortages and a skills gap in cybersecurity. As a result, many cybersecurity product vendors and security organizations have looked to artificial intelligence to shore up their defenses. This work considers how to characterize attackers and defenders in one approach to the automation of cyber defense -- the application of reinforcement learning. Specifically, we characterize the types of attackers and defenders in the sense of Bayesian games and, using reinforcement learning, derive empirical findings about how to best train agents that defend against multiple types of attackers.