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Deep learning is increasingly used as a building block of security systems.
A value for n-person games
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Convolutional networks for images, speech, and time-series
Y. LeCun and Y. Bengio · 1995
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Neural Networks: A Systematic Approach
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Long short-term memory
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Pattern classification
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Malicious PDF detection using metadata and structural features
C. Smutz and A. Stavrou · 2012
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Dissecting android malware: Characterization and evolution
Y. Zhou and X. Jiang · 2012
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Revolver: An automated approach to the detection of evasive web-based malware
A. Kapravelos, Y. Shoshitaishvili, M. Cova, C. Kruegel, and G. Vigna · 2013
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Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
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Drebin: Efficient and explainable detection of Android malware in your pocket
D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck · 2014
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
K. Cho, B. van Merrienboer, Ç. Gülçehre, F. Bougares, H. Schwenk, and Y. Bengio · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Practical evasion of a learning-based classifier: A case study
N. Šrndić and P. Laskov · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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Recognizing functions in binaries with neural networks
E. C. R. Shin, D. Song, and R. Moazzezi · 2015
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Striving for simplicity: The all convolutional net
J. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
A. Datta, S. Sen, and Y. Zick · 2016
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CVXPY: A Python-embedded modeling language for convex optimization
S. Diamond and S. Boyd · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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MtNet: A multi-task neural network for dynamic malware classification
W. Huang and J. W. Stokes · 2016
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Neural network-based graph embedding for cross-platform binary code similarity detection
X. Xu, C. Liu, Q. Feng, H. Yin, L. Song, and D. Song · 2017
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M. Alber, S. Lapuschkin, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K.-R. Müller, S. Dähne, and P.-J. Kindermans · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2018
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"why should i trust you?": Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
Cited alongside, same era.
"what is relevant in a text document?": An interpretable machine learning approach
L. Arras, F. Horn, G. Montavon, K.-R. Müller, and W. Samek · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. A. Wagner · 2017
Cited alongside, same era.
Neural nets can learn function type signatures from binaries
Z. L. Chua, S. Shen, P. Saxena, and Z. Liang · 2017
Cited alongside, same era.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhyay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2018
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LEMNA: Explaining deep learning based security applications
W. Guo, D. Mu, J. Xu, P. Su, G. Wang, and X. Xing · 2018
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Learning how to explain neural networks: Patternnet and patternattribution
P. jan Kindermans, K. T. Schütt, M. Alber, K.-R. Müller, D. Erhan, B. Kim, and S. Dähne · 2018
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Vuldeepecker: A deep learning-based system for vulnerability detection
Z. Li, D. Zou, S. Xu, X. Ou, H. Jin, S. Wang, Z. Deng, and Y. Zhong · 2018
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Android/FakeInstaller.L
McAfee · 2018
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Sok: Security and privacy in machine learning
N. Papernot, P. D. McDaniel, A. Sinha, and M. P. Wellman · 2018
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Is AmI (attacks meet interpretability) robust to adversarial examples?
N. Carlini · 2019
Closest in time.
Explanations can be manipulated and geometry is to blame
A.-K. Dombrowski, M. Alber, C. J. Anders, M. Ackermann, K.-R. Müller, and P. Kessel · 2019
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DroidDream mobile malware
J. Foremost · 2019
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Security Alert: New sophisticated Android malware DroidKungFu found in alternative chinese App markets
X. Jiang · 2019
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Security Alert: New Android malware GoldDream found in alternative app markets
X. Jiang · 2019
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju · 2019
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Interpretable deep learning under fire
X. Zhang, N. Wang, H. Shen, S. Ji, X. Luo, and T. Wang · 2019
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