How to Train your Antivirus: RL-based Hardening through the
Problem-Space

How to Train your Antivirus: RL-based Hardening through the Problem-Space

29 February 2024

Jacopo Cortellazzi

Ilias Tsingenopoulos

Davy Preuveneers

Lorenzo Cavallaro

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Papers citing "How to Train your Antivirus: RL-based Hardening through the Problem-Space"

18 / 18 papers shown

Title
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance Savino Dambra Yufei Han Simone Aonzo Platon Kotzias Antonino Vitale Juan Caballero Davide Balzarotti Leyla Bilge 68 24 0 27 Jul 2023
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data Tomás Pevný Viliam Lisý B. Bosanský P. Somol Michal Pěchouček 74 1 0 04 Aug 2022
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks Salijona Dyrmishi Salah Ghamizi Thibault Simonetto Yves Le Traon Maxime Cordy AAML 67 18 0 07 Feb 2022
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay Shalmali Joshi Himabindu Lakkaraju 54 109 0 26 Feb 2021
Realizable Universal Adversarial Perturbations for Malware Raphael Labaca-Castro Luis Muñoz-González Feargus Pendlebury Gabi Dreo Rodosek Fabio Pierazzi Lorenzo Cavallaro AAML 49 6 0 12 Feb 2021
Adversarial Examples in Constrained Domains Ryan Sheatsley Nicolas Papernot Mike Weisman Gunjan Verma Patrick McDaniel AAML 59 23 0 02 Nov 2020
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 209 2,052 0 16 Apr 2020
Functionality-preserving Black-box Optimization of Adversarial Windows Malware Christian Scano Battista Biggio Giovanni Lagorio Fabio Roli A. Armando AAML 54 145 0 30 Mar 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 277 834 0 19 Feb 2020
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 91 1,838 0 06 May 2019
AutoAugment: Learning Augmentation Policies from Data E. D. Cubuk Barret Zoph Dandelion Mané Vijay Vasudevan Quoc V. Le 131 1,772 0 24 May 2018
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning Hyrum S. Anderson Anant Kharkar Bobby Filar David Evans P. Roth AAML 73 210 0 26 Jan 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 128 1,409 0 08 Dec 2017
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 217 1,842 0 30 Nov 2017
Malware Detection by Eating a Whole EXE Edward Raff Jon Barker Jared Sylvester Robert Brandon Bryan Catanzaro Charles K. Nicholas 65 545 0 25 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,069 0 19 Jun 2017
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN Weiwei Hu Ying Tan GAN 73 461 0 20 Feb 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,555 0 16 Aug 2016