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arXiv · 1808.05770

Reinforcement Learning for Autonomous Defence in Software-Defined Networking

Yi Han·Benjamin I. P. Rubinstein·Tamas Abraham·Tansu Alpcan·Olivier De Vel·Sarah Erfani·David Hubczenko·Christopher Leckie·Paul Montague
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Abstract

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.

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Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague. 2018-08-17. Reinforcement Learning for Autonomous Defence in Software-Defined Networking. https://arxiv.org/abs/1808.05770

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