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Reinforcement Learning for Autonomous Defence in Software-Defined Networking

  • Yi HanEmail author
  • Benjamin I. P. Rubinstein
  • Tamas Abraham
  • Tansu Alpcan
  • Olivier De Vel
  • Sarah Erfani
  • David Hubczenko
  • Christopher Leckie
  • Paul Montague
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11199)

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.

Keywords

Adversarial reinforcement learning Software-defined networking Cyber security Adversarial training 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computing and Information SystemsThe University of MelbourneParkvilleAustralia
  2. 2.Defence Science and Technology GroupEdinburghAustralia

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