# Network Environment Design for Autonomous Cyberdefense

arXiv: https://arxiv.org/abs/2103.07583 · HTML: https://mlsanity.com/paper/2103.07583/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2021.

## Built on by (most cited first, 30 of 32)

- [Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation](https://mlsanity.com/paper/2207.12355/) (2022)
- [Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies](https://mlsanity.com/paper/2211.15557/) (2022)
- [Automated Cyber Defence: A Review](https://mlsanity.com/paper/2303.04926/) (2023)
- [NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios](https://mlsanity.com/paper/2305.17246/) (2023)
- [Reward Shaping for Happier Autonomous Cyber Security Agents](https://mlsanity.com/paper/2310.13565/) (2023)
- [Intrusion Prevention through Optimal Stopping](https://mlsanity.com/paper/2111.00289/) (2021)
- [Inroads into Autonomous Network Defence using Explained Reinforcement Learning](https://mlsanity.com/paper/2306.09318/) (2023)
- [Learning Near-Optimal Intrusion Responses Against Dynamic Attackers](https://mlsanity.com/paper/2301.06085/) (2023)
- [Large Language Models are Autonomous Cyber Defenders](https://mlsanity.com/paper/2505.04843/) (2025)
- [Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.05939/) (2023)
- [Entity-based Reinforcement Learning for Autonomous Cyber Defence](https://mlsanity.com/paper/2410.17647/) (2024)
- [Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey](https://mlsanity.com/paper/2310.07745/) (2023)
- [Network Defense is Not a Game](https://mlsanity.com/paper/2104.10262/) (2021)
- [Learning Security Strategies through Game Play and Optimal Stopping](https://mlsanity.com/paper/2205.14694/) (2022)
- [CSLE: A Reinforcement Learning Platform for Autonomous Security Management](https://mlsanity.com/paper/2604.15590/) (2026)
- [Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception](https://mlsanity.com/paper/2111.11932/) (2021)
- [Designing Robust Cyber-Defense Agents with Evolving Behavior Trees](https://mlsanity.com/paper/2410.16383/) (2024)
- [Enabling A Network AI Gym for Autonomous Cyber Agents](https://mlsanity.com/paper/2304.01366/) (2023)
- [The Path To Autonomous Cyber Defense](https://mlsanity.com/paper/2404.10788/) (2024)
- [Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents](https://mlsanity.com/paper/2412.02875/) (2024)
- [Building Better Environments for Autonomous Cyber Defence](https://mlsanity.com/paper/2604.08805/) (2026)
- [Deception for Cyber Defence: Challenges and Opportunities](https://mlsanity.com/paper/2208.07127/) (2022)
- [Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms](https://mlsanity.com/paper/2506.08192/) (2025)
- [Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents](https://mlsanity.com/paper/2304.01244/) (2023)
- [A Red Teaming Framework for Evaluating Robustness of AI-enabled Security Orchestration, Automation, and Response Systems](https://mlsanity.com/paper/2605.17075/) (2026)
- [Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctions](https://mlsanity.com/paper/2509.10983/) (2025)
- [SubstratumGraphEnv: Reinforcement Learning Environment (RLE) for Modeling System Attack Paths](https://mlsanity.com/paper/2603.01340/) (2026)
- [Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents](https://mlsanity.com/paper/2606.18223/) (2026)
- [COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies](https://mlsanity.com/paper/2606.30479/) (2026)
- [BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense](https://mlsanity.com/paper/2609.11852/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) has been demonstrated suitable to develop agents that play complex games with human-level performance. However, it is not understood how to effectively use RL to perform cybersecurity tasks. To develop such understanding, it is necessary to develop RL agents using simulation and emulation systems allowing researchers to model a broad class of realistic threats and network conditions. Demonstrating that a specific RL algorithm can be effective for defending a network under certain conditions may not necessarily give insight about the performance of the algorithm when the threats, network conditions, and security goals change. This paper introduces a novel approach for network environment design and a software framework to address the fundamental problem that network defense cannot be defined as a single game with a simple set of fixed rules. We show how our approach is necessary to facilitate the development of RL network defenders that are robust against attacks aimed at the agent's learning. Our framework enables the development and simulation of adversaries with sophisticated behavior that includes poisoning and evasion attacks on RL network defenders.