Conference Proceedings

Reinforcement learning for autonomous defence in software-defined networking

Y Han, BIP Rubinstein, T Abraham, T Alpcan, O De Vel, S Erfani, D Hubczenko, C Leckie, P Montague

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Springer | Published : 2018

Abstract

© 2018, Springer Nature Switzerland AG. 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..

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