The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?

A. W. Moore and D. Zuev, “Internet traffic classification using Bayesian analysis techniques,” in ACM SIGMETRICS Performance Evaluation Review , vol. 33, no. 1. ACM, 2005, pp. 50–60

2005

Earlier work this paper cites.

R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” in Proceedings of the 2010 IEEE Symposium on Security and Privacy (SP) . IEEE, 2010, pp. 305–316

2010

Earlier work this paper cites.

I. Corona, G. Giacinto, and F. Roli, “Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues,” Information Sciences , vol. 239, pp. 201–225, 2013

2013

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,” 2014

2014

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014

Original

2014

Earlier work this paper cites.

N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in Proceedings of the 2016 IEEE European Symposium on Security and Privacy (EuroS&P) , 2016, pp. 372–387

2016

Cited alongside, same era.

N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in Proceedings of the 2016 IEEE Symposium on Security and Privacy (SP) . IEEE, 2016, pp. 582–597

2016

Cited alongside, same era.

N. Feamster and J. Rexford, “Why (and how) networks should run themselves,” arXiv preprint arXiv:1710.11583 , 2017

Original

2017

Cited alongside, same era.

N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 39–57

2017

Cited alongside, same era.

M. E. Ahmed and H. Kim, “Poster: Adversarial examples for classifiers in high-dimensional network data,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 2467–2469

2017

Later among the works it cites.

I. Goodfellow, P. McDaniel, and N. Papernot, “Making machine learning robust against adversarial inputs,” Communications of the ACM , vol. 61, no. 7, pp. 56–66, 2018

2018

Later among the works it cites.

Y. Vorobeychik and M. Kantarcioglu, “Adversarial machine learning,” Synthesis Lectures on Artificial Intelligence and Machine Learning , vol. 12, no. 3, pp. 1–169, 2018

2018

Later among the works it cites.

B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern Recognition , vol. 84, pp. 317–331, 2018

2018

Later among the works it cites.