Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Harley, T. P. Lillicrap, D. Silver, and K. Kavukcuoglu, “Asynchronous Methods for Deep Reinforcement Learning,” in ICML , 2016, pp. 1928–1937

1937

Earlier work this paper cites.

R. S. Sutton and A. G. Barto, Introduction to Reinforcement Learning , 1st ed. Cambridge, MA, USA: MIT Press, 1998

1998

Earlier work this paper cites.

M. Barreno, B. Nelson, A. D. Joseph, and J. D. Tygar, “The Security of Machine Learning,” Machine Learning , vol. 81, no. 2, pp. 121–148, Nov. 2010

2010

Earlier work this paper cites.

L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. Tygar, “Adversarial machine learning,” in AISec . ACM, 2011, pp. 43–58

2011

Earlier work this paper cites.

B. Biggio, B. Nelson, and P. Laskov, “Poisoning Attacks against Support Vector Machines,” in ICML , Scotland, 2012, pp. 1467–1474

2012

Earlier work this paper cites.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing Properties of Neural Networks,” arXiv:1312.6199 , 2013

Original

2013

Earlier work this paper cites.

“SDN architecture,” Tech. Rep., Jun. 2014. [Online]. Available: https://www.opennetworking.org/wp-content/uploads/2013/02/TR_SDN_ARCH_1.0_06062014.pdf

2013

Earlier work this paper cites.

J. Medved, R. Varga, A. Tkacik, and K. Gray, “OpenDaylight: Towards a Model-Driven SDN Controller architecture,” in IEEE WoWMoM , Jun. 2014, pp. 1–6

2014

Earlier work this paper cites.

B. Biggio, I. Corona, B. Nelson, B. I. Rubinstein, D. Maiorca, G. Fumera, G. Giacinto, and F. Roli, “Security evaluation of support vector machines in adversarial environments,” in Support Vector Machines Applications . Springer, 2014, pp. 105–153

2014

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adversarial Examples,” arXiv:1412.6572 , 2014

Original

2014

Earlier work this paper cites.

H. V. Hasselt, A. Guez, and D. Silver, “Deep Reinforcement Learning with Double Q-learning,” arXiv:1509.06461 , Sep. 2015

Original

2015

Earlier work this paper cites.

T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized Experience Replay,” CoRR , vol. abs/1511.05952, 2015

Original

2015

Cited alongside, same era.

A. Nguyen, J. Yosinski, and J. Clune, “Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images,” in CVPR , 2015, pp. 427–436

2015

Cited alongside, same era.

N. Papernot, P. McDaniel, and I. Goodfellow, “Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples,” arXiv:1605.07277 , 2016

Original

2016

Cited alongside, same era.

N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The Limitations of Deep Learning in Adversarial Settings,” in EuroS&P , 2016, pp. 372–387

2016

Cited alongside, same era.

S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal Adversarial Perturbations,” arXiv:1610.08401 , 2016

“Mininet: An Instant Virtual Network on your Laptop,” http://mininet.org/, 2017

2017

Later among the works it cites.

N. Papernot, P. D. McDaniel, I. J. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in ACM on AsiaCCS, 2017 . ACM, 2017, pp. 506–519

2017

Later among the works it cites.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv:1706.06083 , 2017

Original

2017

Later among the works it cites.

A. Mandlekar, Y. Zhu, A. Garg, L. Fei-Fei, and S. Savarese, “Adversarially robust policy learning: Active construction of physically-plausible perturbations,” in 2017 IROS , 2017, pp. 3932–3939

2017

Later among the works it cites.

Y.-C. Lin, M.-Y. Liu, M. Sun, and J.-B. Huang, “Detecting adversarial attacks on neural network policies with visual foresight,” CoRR , vol. abs/1710.00814, 2017