Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics

2010

Earlier work this paper cites.

R. Roman, P. Najera, and J. Lopez, “Securing the internet of things,” IEEE Computer, Vol 44

2011

Earlier work this paper cites.

Y. A. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller, “Efficient backprop,” in Neural networks: Tricks of the trade

2012

Earlier work this paper cites.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” ICLR

Original

2014

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” CoRR

Original

2015

Earlier work this paper cites.

A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” in Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

2016

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 2016 IEEE European Symposium on Security and Privacy (EuroS&P)

2016

Cited alongside, same era.

E. Hodo, X. Bellekens, A. Hamilton, P.-L. Dubouilh, E. Iorkyase, C. Tachtatzis, and R. Atkinson, “Threat analysis of iot networks using artificial neural network intrusion detection system,” in 2016 International Symposium on Networks, Computers and Communications (ISNCC)

2016

Cited alongside, same era.

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-normalizing neural networks,” in Advances in neural information processing systems

2017

Cited alongside, same era.

Z. Gong, W. Wang, and W.-S. Ku, “Adversarial and clean data are not twins,” arXiv preprint arXiv:1704.04960

Original

2017

Cited alongside, same era.

N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,” arXiv preprint arXiv:1811.00701

Original

2018

Later among the works it cites.

A. Warzyński and G. Kołaczek, “Intrusion detection systems vulnerability on adversarial examples,” in 2018 Innovations in Intelligent Systems and Applications (INISTA)

2018

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,” in International Conference on Learning Representations

2018

Later among the works it cites.

M.-I. Nicolae, M. Sinn, M. N. Tran, A. Rawat, M. Wistuba, V. Zantedeschi, I. M. Molloy, and B. Edwards, “Adversarial robustness toolbox v0. 2.2,” arXiv preprint arXiv:1807.01069

Original

2018

Later among the works it cites.