secml-malware: Pentesting Windows Malware Classifiers with Adversarial
EXEmples in Python

secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python

26 April 2021

Battista Biggio

Papers citing "secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python"

6 / 6 papers shown

Title
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection Marco Rando Luca Demetrio Lorenzo Rosasco Fabio Roli AAML 24 1 0 23 May 2024
Bayesian Learned Models Can Detect Adversarial Malware For Free Bao Gia Doan Dang Quang Nguyen Paul Montague Tamas Abraham O. Vel S. Çamtepe S. Kanhere Ehsan Abbasnejad D. Ranasinghe AAML 33 1 0 27 Mar 2024
A Comparison of Adversarial Learning Techniques for Malware Detection Pavla Louthánová M. Kozák M. Jureček Mark Stamp AAML 16 2 0 19 Aug 2023
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion Zhuoqun Huang Neil G. Marchant Keane Lucas Lujo Bauer O. Ohrimenko Benjamin I. P. Rubinstein AAML 22 14 0 31 Jan 2023
A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries P. Dasgupta Zachary Osman AAML 20 2 0 22 Nov 2021
secml: A Python Library for Secure and Explainable Machine Learning Maura Pintor Luca Demetrio Angelo Sotgiu Marco Melis Ambra Demontis Battista Biggio AAML 15 15 0 20 Dec 2019