Reinforcement Learning for Autonomous Defence in Software-Defined Networking
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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.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2018.
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Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 1; 2025: 2; 2026: 1.
17 papers.
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- 2022Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learningcited by 21
- 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learningcited by 17
- 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackerscited by 13
- 2022A Search-Based Testing Approach for Deep Reinforcement Learning Agentscited by 12
- 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learningcited by 9
- 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 8
- 2023Scalable Learning of Intrusion Responses through Recursive Decompositioncited by 8
- 2021Network Defense is Not a Gamecited by 6
- 2021Automating Privilege Escalation with Deep Reinforcement Learningcited by 5
- 2022Deep-Attack over the Deep Reinforcement Learningcited by 5
- 2025Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantizationcited by 5
- 2025Position: Certified Robustness Does Not (Yet) Imply Model Securitycited by 4
5 more
- 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecuritycited by 4
- 2021On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methodscited by 2
- 2022Model-Free Deep Reinforcement Learning in Software-Defined Networkscited by 1
- 2023Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networkscited by 0
- 2024Exploring reinforcement learning for incident response in autonomous military vehiclescited by 0
- 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity9 Feb 2026
- 2025Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization20 Aug 2025
- 2025Position: Certified Robustness Does Not (Yet) Imply Model Security16 Jun 2025
- 2024Exploring reinforcement learning for incident response in autonomous military vehicles28 Oct 2024
- 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning8 Dec 2023
- 2023Scalable Learning of Intrusion Responses through Recursive Decomposition6 Sep 2023
- 2023Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks9 Aug 2023
- 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learning15 Jun 2023
- 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackers11 Jan 2023
- 2022Model-Free Deep Reinforcement Learning in Software-Defined Networks3 Sep 2022
- 2022A Search-Based Testing Approach for Deep Reinforcement Learning Agents15 Jun 2022
- 2022Deep-Attack over the Deep Reinforcement Learning2 May 2022
5 more
- 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions27 Apr 2022
- 2022Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning17 Apr 2022
- 2021On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods8 Nov 2021
- 2021Automating Privilege Escalation with Deep Reinforcement Learning4 Oct 2021
- 2021Network Defense is Not a Game20 Apr 2021
- 2022Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learningcited by 21
- 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learningcited by 17
- 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackerscited by 13
- 2022A Search-Based Testing Approach for Deep Reinforcement Learning Agentscited by 12
- 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learningcited by 9
- 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 8
- 2023Scalable Learning of Intrusion Responses through Recursive Decompositioncited by 8
- 2021Network Defense is Not a Gamecited by 6
- 2021Automating Privilege Escalation with Deep Reinforcement Learningcited by 5
- 2022Deep-Attack over the Deep Reinforcement Learningcited by 5
- 2025Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantizationcited by 5
- 2025Position: Certified Robustness Does Not (Yet) Imply Model Securitycited by 4
12 most cited of 17 All 17 (JSON)
- 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity9 Feb 2026
- 2025Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization20 Aug 2025
- 2025Position: Certified Robustness Does Not (Yet) Imply Model Security16 Jun 2025
- 2024Exploring reinforcement learning for incident response in autonomous military vehicles28 Oct 2024
- 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning8 Dec 2023
- 2023Scalable Learning of Intrusion Responses through Recursive Decomposition6 Sep 2023
- 2023Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks9 Aug 2023
- 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learning15 Jun 2023
- 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackers11 Jan 2023
- 2022Model-Free Deep Reinforcement Learning in Software-Defined Networks3 Sep 2022
- 2022A Search-Based Testing Approach for Deep Reinforcement Learning Agents15 Jun 2022
- 2022Deep-Attack over the Deep Reinforcement Learning2 May 2022
12 newest of 17 All 17 (JSON)