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[1] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[2] Adam Doupé,et al. Deep Android Malware Detection , 2017, CODASPY.
[3] Stephen P. Boyd,et al. CVXPY: A Python-Embedded Modeling Language for Convex Optimization , 2016, J. Mach. Learn. Res..
[4] Klaus-Robert Müller,et al. "What is relevant in a text document?": An interpretable machine learning approach , 2016, PloS one.
[5] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[6] Dan Arp,et al. Drebin : � Efficient and Explainable Detection of Android Malware in Your Pocket , 2014 .
[7] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[8] Yarin Gal,et al. Real Time Image Saliency for Black Box Classifiers , 2017, NIPS.
[9] Yajin Zhou,et al. Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.
[10] Pavel Laskov,et al. Practical Evasion of a Learning-Based Classifier: A Case Study , 2014, 2014 IEEE Symposium on Security and Privacy.
[11] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[12] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[13] Shouhuai Xu,et al. VulDeePecker: A Deep Learning-Based System for Vulnerability Detection , 2018, NDSS.
[15] Cengiz Öztireli,et al. Towards better understanding of gradient-based attribution methods for Deep Neural Networks , 2017, ICLR.
[16] Zhenkai Liang,et al. Neural Nets Can Learn Function Type Signatures From Binaries , 2017, USENIX Security Symposium.
[17] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[18] Le Song,et al. Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection , 2018 .
[19] Klaus-Robert Müller,et al. Learning how to explain neural networks: PatternNet and PatternAttribution , 2017, ICLR.
[20] Patrick D. McDaniel,et al. Adversarial Examples for Malware Detection , 2017, ESORICS.
[21] Andrea Vedaldi,et al. Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[22] Christopher Krügel,et al. Revolver: An Automated Approach to the Detection of Evasive Web-based Malware , 2013, USENIX Security Symposium.
[23] Wenyi Huang,et al. MtNet: A Multi-Task Neural Network for Dynamic Malware Classification , 2016, DIMVA.
[24] Angelos Stavrou,et al. Malicious PDF detection using metadata and structural features , 2012, ACSAC '12.
[25] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[26] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[27] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[28] Gang Wang,et al. LEMNA: Explaining Deep Learning based Security Applications , 2018, CCS.
[29] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[30] Alexander Binder,et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.
[31] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[32] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[33] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[34] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[35] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[36] Michael P. Wellman,et al. SoK: Security and Privacy in Machine Learning , 2018, 2018 IEEE European Symposium on Security and Privacy (EuroS&P).
[37] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Dawn Xiaodong Song,et al. Recognizing Functions in Binaries with Neural Networks , 2015, USENIX Security Symposium.
[39] Nicholas Carlini,et al. Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples? , 2019, ArXiv.
[40] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[41] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.