Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence

no code implementations • 25 Feb 2019 • Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah Erfani

Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting.

reinforcement-learning Reinforcement Learning (RL)

Reinforcement Learning for Autonomous Defence in Software-Defined Networking

no code implementations • 17 Aug 2018 • Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague

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.

BIG-bench Machine Learning General Classification +2