When the Guard failed the Droid: A case study of Android malware

31 March 2020

Papers citing "When the Guard failed the Droid: A case study of Android malware"

21 / 21 papers shown

Title
Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning Zhuo Ma Haoran Ge Zhuzhu Wang Yang Liu Ximeng Liu 31 38 0 10 Feb 2020
Deep Learning Based Android Malware Detection Framework Soumya Sourav Devashish Khulbe Naman Kapoor 14 2 0 10 Dec 2019
DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling Muhammad Ikram Pierrick Beaume M. Kâafar 38 33 0 22 May 2019
Don't Pick the Cherry: An Evaluation Methodology for Android Malware Detection Methods Aleieldin Salem Sebastian Banescu A. Pretschner 26 10 0 25 Mar 2019
Adversarial Samples on Android Malware Detection Systems for IoT Systems Xiaolei Liu Xiaojiang Du Xiaosong Zhang Qingxin Zhu Mohsen Guizani AAML 25 61 0 12 Feb 2019
A Comparative Analysis of Android Malware Neeraj Chavan Fabio Di Troia Mark Stamp 16 13 0 21 Jan 2019
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection Xiao Chen Chaoran Li Derui Wang S. Wen Jun Zhang Surya Nepal Yang Xiang K. Ren AAML 45 245 0 10 Aug 2018
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time Feargus Pendlebury Fabio Pierazzi Roberto Jordaney Johannes Kinder Lorenzo Cavallaro 41 354 0 20 Jul 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Percy Liang AAML 85 967 0 29 Jan 2018
Adversarial Examples: Attacks and Defenses for Deep Learning Xiaoyong Yuan Pan He Qile Zhu Xiaolin Li SILM AAML 64 1,614 0 19 Dec 2017
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version) Lucky Onwuzurike Enrico Mariconti Panagiotis Andriotis Emiliano De Cristofaro Gordon J. Ross Gianluca Stringhini 15 167 0 20 Nov 2017
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 94 787 0 30 Oct 2017
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features Liang Tong Yue Liu Chen Hajaj Chaowei Xiao Ning Zhang Yevgeniy Vorobeychik AAML OOD 28 87 0 28 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 227 11,962 0 19 Jun 2017
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection Ambra Demontis Marco Melis Battista Biggio Davide Maiorca Dan Arp Konrad Rieck Igino Corona Giorgio Giacinto Fabio Roli AAML 36 284 0 28 Apr 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 62 710 0 21 Feb 2017
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN Weiwei Hu Ying Tan GAN 60 461 0 20 Feb 2017
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 491 5,878 0 08 Jul 2016
Adversarial Perturbations Against Deep Neural Networks for Malware Classification Kathrin Grosse Nicolas Papernot Praveen Manoharan Michael Backes Patrick McDaniel AAML 44 418 0 14 Jun 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 49 3,660 0 08 Feb 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 163 18,922 0 20 Dec 2014