Towards Interpretable Ensemble Learning for Image-based Malware Detection

13 January 2021

Papers citing "Towards Interpretable Ensemble Learning for Image-based Malware Detection"

23 / 23 papers shown

Title
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 52 134 0 14 Feb 2021
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis Ming Fan Wenying Wei Xiaofei Xie Yang Liu X. Guan Ting Liu FAtt AAML 68 37 0 13 Aug 2020
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation Bozhi Wu Sen Chen Cuiyun Gao Lingling Fan Yang Liu W. Wen Michael R. Lyu 47 56 0 24 Apr 2020
SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning Ji Wang Qi Jing Jianbo Gao AAML 13 18 0 06 Sep 2019
The Curious Case of Machine Learning In Malware Detection Sherif Saad William Briguglio H. Elmiligi AAML 34 45 0 18 May 2019
Visualizing Deep Networks by Optimizing with Integrated Gradients Zhongang Qi Saeed Khorram Fuxin Li FAtt 50 123 0 02 May 2019
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 37 215 0 04 Apr 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation Marco Ancona Cengiz Öztireli Markus Gross FAtt TDI 59 224 0 26 Mar 2019
Deep Transfer Learning for Static Malware Classification Li-Wei Chen 33 46 0 18 Dec 2018
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks Siyue Wang Tianlin Li Pu Zhao Wujie Wen David Kaeli S. Chin Xinyu Lin AAML 46 70 0 13 Sep 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models Jacob R. Kauffmann K. Müller G. Montavon DRL 64 96 0 16 May 2018
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures Abhronil Sengupta Yuting Ye Robert Y. Wang Chiao Liu Kaushik Roy 76 997 0 07 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 101 3,922 0 06 Feb 2018
Imbalanced Malware Images Classification: a CNN based Approach Songqing Yue Tianyang Wang 31 112 0 27 Aug 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 196 2,215 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 692 21,613 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 69 1,514 0 11 Apr 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 144 3,848 0 10 Apr 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 234 19,796 0 07 Oct 2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size F. Iandola Song Han Matthew W. Moskewicz Khalid Ashraf W. Dally Kurt Keutzer 132 7,448 0 24 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 746 16,828 0 16 Feb 2016
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 120 1,189 0 21 Sep 2015
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 389 15,825 0 12 Nov 2013