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Recently researchers have proposed using deep learning-based systems for malware detection.
R. Pascanu, J. W. Stokes, H. Sanossian, M. Marinescu, and A. Thomas, “Malware classification with recurrent networks,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2015, pp. 1916–1920
1920
Earlier work this paper cites.
J. Crandall, Z. Su, F. Chong, and S. Wu, “On deriving unknown vulnerabilities from zero-day polymorphic and metamorphic worm exploits,” in Proceedings of the ACM Conference on Computer and Communications Security (CCS’05) , 2005, pp. 235–248
2005
Earlier work this paper cites.
P. Li, T. J. Hastie, and K. W. Church, “Very sparse random projections,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ICDM) , 2006, pp. 287–296
2006
Earlier work this paper cites.
C. D. Manning, P. Raghavan, and H. Schutze, An Introduction to Information Retrieval . Cambridge University Press, 2009
2009
Earlier work this paper cites.
C. Smutz and A. Stavrou, “Malicious pdf detection using metadata and structural features,” Technical report , 2012
2012
Earlier work this paper cites.
G. E. Dahl, J. W. Stokes, L. Deng, and D. Yu, “Large-scale malware classification using random projections and neural networks,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2013, pp. 3422–3426
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929–1958, Jan. 2014. [Online]. Available: http://dl.acm.org/citation.cfm?id=2627435.2670313
2014
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” Neural Information Processing Systems (NIPS) Deep Learning Workshop , 2014
2014
Earlier work this paper cites.
J. Saxe and K. Berlin, “Deep neural network based malware detection using two-dimensional binary program features,” Malware Conference (MALCON) , 2015
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” Proceedings of the International Conference on Learning Representations (ICML) , 2015
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 427–436
2015
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,” Proceedings of the 37th IEEE Symposium on Security and Privacy , 2015
2015
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,” Proceedings of the 1st IEEE European Symposium on Security and Privacy , 2015
2015
Cited alongside, same era.
F. Seide and A. Agarwal, “Cntk: Microsoft’s open-source deep-learning toolkit,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2016, pp. 2135–2135
2016
Later among the works it cites.
B. Athiwaratkun and J. W. Stokes, “Malware classification with lstm and gru language models and a character-level cnn,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , March 2017, pp. 2482–2486
2017
Closest in time.
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,” Proceedings of the ACM Asia Conference on Computer and Communications Security , 2017
2017
Closest in time.
2017
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W. Huang and J. W. Stokes, “Mtnet: A multi-task neural network for dynamic malware classfication,” in Proceedings of Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) , 2016, pp. 399–418
2016
Cited alongside, same era.
B. Kolosnjaji, A. Zarras, G. Webster, and C. Eckert, “Deep learning for classification of malware system call sequences,” in Australasian Joint Conference on Artificial Intelligence . Springer International Publishing, 2016, pp. 137–149
2016
Cited alongside, same era.
W. Xu, Y. Qi, and D. Evans, “Automatically evading classifiers,” Proceedings of the Network and Distributed System Security Symposium (NDSS) , 2016
2016
Cited alongside, same era.
A. Kantchelian, J.D.Tygar, and A. D.Joseph, “Evasion and hardening of tree ensemble classifiers,” Proceedings of the International Conference on Machine Learning (ICML) , 2016
2016
Cited alongside, same era.
F. Tramer, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis,” Proceedings of the USENIX Security Symposium , 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Closest in time.
2017
Closest in time.
2017
Closest in time.
W. Hu and Y. Tan, “Generating adversarial malware examples for black-box attacks based on gan,” arXiv preprint 1702.05983 , 2017
2017
Closest in time.
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel, “Adversarial perturbations against deep neural networks for malware classification,” in Proceedings of the European Symposium on Research in Computer Security (ESORICS) , 2017
2017
Closest in time.