HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples

T. K. Ho, “The random subspace method for constructing decision forests,” IEEE Transactions on PAMI , vol. 20, no. 8, pp. 832–844, 1998

1998

Earlier work this paper cites.

A. Gionis, P. Indyk, and R. Motwani, “Similarity search in high dimensions via hashing,” in VLDB , USA, 1999, pp. 518–529

1999

Earlier work this paper cites.

C.-J. Hsieh, K.-W. Chang, C.-J. Lin, and et al., “A dual coordinate descent method for large-scale linear svm,” in Proceedings of the 25th ICML . New York, NY, USA: ACM, 2008, pp. 408–415

2008

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in NIPS , 2012, pp. 1097–1105

2012

Earlier work this paper cites.

J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research , vol. 13, no. Feb, pp. 281–305, 2012

2012

Earlier work this paper cites.

I. C. B. Biggio and D. M. et al., “Evasion attacks against machine learning at test time,” in Machine Learning and Knowledge Discovery in Databases: European Conference . Springer, 01 2013, pp. 387–402

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,” arXiv preprint arXiv:1312.6199 , 2013

Original

2013

Earlier work this paper cites.

Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE T-PAMI , vol. 35, no. 8, pp. 1798–1828, 2013

2013

Earlier work this paper cites.

P. L. Nedim rndic, “Practical evasion of a learning-based classifier: A case study,” in Security and Privacy (SP), 2014 IEEE Symposium on . IEEE, 2014, pp. 197–211

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.

L. Xu, Z. Zhan, S. Xu, and K. Ye, “An evasion and counter-evasion study in malicious websites detection,” in CNS, 2014 IEEE Conference on . IEEE, 2014, pp. 265–273

2014

Earlier work this paper cites.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in NIPS , 2014, pp. 3104–3112

2014

Earlier work this paper cites.

S. Gu and L. Rigazio, “Towards deep neural network architectures robust to adversarial examples,” arXiv preprint arXiv:1412.5068 , 2014

Original

2014

Earlier work this paper cites.

B. Biggio, G. Fumera, and F. Roli, “Security evaluation of pattern classifiers under attack,” IEEE Transactions on Knowledge and Data Engineering , vol. 26, no. 4, pp. 984–996, April 2014

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR , vol. abs/1412.6980, 2014

Original

2014

Earlier work this paper cites.

D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens, “Drebin: Effective and explainable detection of android malware in your pocket.” in Ndss , vol. 14, 2014, pp. 23–26

2014

Earlier work this paper cites.

T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii, “Distributional smoothing with virtual adversarial training,” arXiv preprint arXiv:1507.00677 , 2015

Original

2015

Earlier work this paper cites.

U. Shaham, Y. Yamada, and S. Negahban, “Understanding adversarial training: Increasing local stability of neural nets through robust optimization,” arXiv preprint arXiv:1511.05432 , 2015

Original

2015

Earlier work this paper cites.

N. Papernot, P. D. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” CoRR , vol. abs/1511.04508, 2015

Original

2015

Earlier work this paper cites.

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks , vol. 61, pp. 85–117, 2015

2015

Earlier work this paper cites.

D.-A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units,” arXiv:1511.07289 , 2015

Original

2015

Earlier work this paper cites.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015

2015

Earlier work this paper cites.

L. Sayfullina, E. Eirola, D. Komashinsky, and et al., “Efficient detection of zero-day android malware using normalized bernoulli naive bayes,” in 2015 IEEE Trustcom/BigDataSE/ISPA , vol. 1, Aug 2015, pp. 198–205

2015

Earlier work this paper cites.

M. Garnaeva, F. Sinitsyn, and Y. Namestnikov, “Overall statistic for 2016,” Blue Book , 2016

2016

Cited alongside, same era.

N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against deep learning systems using adversarial examples,” arXiv preprint , 2016

2016

Cited alongside, same era.

K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel, “Adversarial perturbations against deep neural networks for malware classification,” arXiv preprint arXiv:1606.04435 , 2016

Original

2016

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 preprint arXiv:1605.07277 , 2016

Original

2016

Cited alongside, same era.

Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into transferable adversarial examples and black-box attacks,” arXiv preprint arXiv:1611.02770 , 2016

M. Cissé, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier, “Parseval networks: Improving robustness to adversarial examples,” in Proceedings of the 34th ICML,Australia , 2017, pp. 854–863

2017

Later among the works it cites.

D. Krotov and J. J. Hopfield, “Dense associative memory is robust to adversarial inputs,” CoRR , vol. abs/1701.00939, 2017

Original

2017

Later among the works it cites.

H. Dang, Y. Huang, and E.-C. Chang, “Evading classifiers by morphing in the dark,” in CCS . ACM, 2017, pp. 119–133

2017

Later among the works it cites.

L. Chen, S. Hou, and Y. Ye, “Securedroid: Enhancing security of machine learning-based detection against adversarial android malware attacks,” in ACSAC . USA: ACM, 2017, pp. 362–372

2017

Later among the works it cites.

M. Saleh, E. P. Ratazzi, and S. Xu, “A control flow graph-based signature for packer identification,” in 2017 IEEE Military Communications Conference , 2017, pp. 683–688