# Reinforcement Learning for Autonomous Defence in Software-Defined Networking

arXiv: https://arxiv.org/abs/1808.05770 · HTML: https://mlsanity.com/paper/1808.05770/

## Key facts

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

## Built on by (most cited first, 17 of 17)

- [Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2204.07932/) (2022)
- [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)
- [A Search-Based Testing Approach for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2206.07813/) (2022)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning](https://mlsanity.com/paper/2312.04940/) (2023)
- [Scalable Learning of Intrusion Responses through Recursive Decomposition](https://mlsanity.com/paper/2309.03292/) (2023)
- [Network Defense is Not a Game](https://mlsanity.com/paper/2104.10262/) (2021)
- [Automating Privilege Escalation with Deep Reinforcement Learning](https://mlsanity.com/paper/2110.01362/) (2021)
- [Deep-Attack over the Deep Reinforcement Learning](https://mlsanity.com/paper/2205.00807/) (2022)
- [Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization](https://mlsanity.com/paper/2508.14385/) (2025)
- [Position: Certified Robustness Does Not (Yet) Imply Model Security](https://mlsanity.com/paper/2506.13024/) (2025)
- [SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity](https://mlsanity.com/paper/2602.08690/) (2026)
- [On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods](https://mlsanity.com/paper/2111.04865/) (2021)
- [Model-Free Deep Reinforcement Learning in Software-Defined Networks](https://mlsanity.com/paper/2209.01490/) (2022)
- [Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks](https://mlsanity.com/paper/2308.04909/) (2023)
- [Exploring reinforcement learning for incident response in autonomous military vehicles](https://mlsanity.com/paper/2410.21407/) (2024)

## Abstract (arXiv)

Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification. In this paper, we investigate the feasibility of applying a specific class of machine learning algorithms, namely, reinforcement learning (RL) algorithms, for autonomous cyber defence in software-defined networking (SDN). In particular, we focus on how an RL agent reacts towards different forms of causative attacks that poison its training process, including indiscriminate and targeted, white-box and black-box attacks. In addition, we also study the impact of the attack timing, and explore potential countermeasures such as adversarial training.