Adversarial Detection of Flash Malware: Limitations and Open Issues

v1v2 (latest)

Adversarial Detection of Flash Malware: Limitations and Open Issues

27 October 2017

Ambra Demontis

Battista Biggio

Maria Elena Chiappe

Giorgio Giacinto

ArXiv (abs)PDF HTML

Papers citing "Adversarial Detection of Flash Malware: Limitations and Open Issues"

7 / 7 papers shown

Title
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection Hamid Bostani Veelasha Moonsamy AAML 93 60 0 07 Oct 2021
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference Yang Zheng Xiaoyi Feng Zhaoqiang Xia Xiaoyue Jiang Ambra Demontis Maura Pintor Battista Biggio Fabio Roli AAML 85 22 0 26 Aug 2021
The Threat of Offensive AI to Organizations Yisroel Mirsky Ambra Demontis J. Kotak Ram Shankar Deng Gelei Liu Yang Xinming Zhang Wenke Lee Yuval Elovici Battista Biggio 99 85 0 30 Jun 2021
Arms Race in Adversarial Malware Detection: A Survey Deqiang Li Qianmu Li Yanfang Ye Shouhuai Xu AAML 103 52 0 24 May 2020
MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers Wei Song Xuezixiang Li Sadia Afroz D. Garg Dmitry Kuznetsov Heng Yin AAML 117 27 0 06 Mar 2020
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables Bojan Kolosnjaji Ambra Demontis Battista Biggio Davide Maiorca Giorgio Giacinto Claudia Eckert Fabio Roli AAML 70 318 0 12 Mar 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 175 1,411 0 08 Dec 2017