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Machine learning has been increasingly used as a first line of defense for Windows malware detection.
DEAP: Evolutionary algorithms made easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné · 2012
Earlier work this paper cites.
Deep neural network based malware detection using two dimensional binary program features
Joshua Saxe and Konstantin Berlin · 2015
Earlier work this paper cites.
Evading machine learning malware detection
Hyrum S Anderson, Anant Kharkar, Bobby Filar, and Phil Roth · 2017
Earlier work this paper cites.
Ember: an open dataset for training static pe malware machine learning models
Hyrum S Anderson and Phil Roth · 2018
Earlier work this paper cites.
Adversarial malware binaries: Evading deep learning for malware detection in executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, and Fabio Roli · 2018
Earlier work this paper cites.
Deceiving end-to-end deep learning malware detectors using adversarial examples
Felix Kreuk, Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Joseph Keshet · 2018
Earlier work this paper cites.
Malware detection by eating a whole exe
Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, and Charles K Nicholas · 2018
Earlier work this paper cites.
Aimed: Evolving malware with genetic programming to evade detection
Raphael Labaca Castro, Corinna Schmitt, and Gabi Dreo · 2019
Earlier work this paper cites.
Activation analysis of a byte-based deep neural network for malware classification
Scott E Coull and Christopher Gardner · 2019
Cited alongside, same era.
Explaining vulnerabilities of deep learning to adversarial malware binaries
Luca Demetrio, Battista Biggio, Giovanni Lagorio, Fabio Roli, and Alessandro Armando · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Optimization-guided binary diversification to mislead neural networks for malware detection
Mahmood Sharif, Keane Lucas, Lujo Bauer, Michael K Reiter, and Saurabh Shintre · 2019
Cited alongside, same era.
Exploring adversarial examples in malware detection
Octavian Suciu, Scott E Coull, and Jeffrey Johns · 2019
Cited alongside, same era.
Adversarial xai methods in cybersecurity
Aditya Kuppa and Nhien-An Le-Khac · 2021
Closest in time.
secml: Secure and explainable machine learning in python
Maura Pintor, Luca Demetrio, Angelo Sotgiu, Marco Melis, Ambra Demontis, and Battista Biggio · 2022
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Quo vadis: hybrid machine learning meta-model based on contextual and behavioral malware representations
Dmitrijs Trizna · 2022
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Optimization of code caves in malware binaries to evade machine learning detectors
Javier Yuste, Eduardo G Pardo, and Juan Tapiador · 2022
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Certified robustness of static deep learning-based malware detectors against patch and append attacks
Daniel Gibert, Giulio Zizzo, and Quan Le · 2023
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Stealing and evading malware classifiers and antivirus at low false positive conditions
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Functionality-preserving black-box optimization of adversarial windows malware, 2020
Luca Demetrio, Battista Biggio, Giovanni Lagorio, Fabio Roli, and Alessandro Armando · 2020
Cited alongside, same era.
Against all odds: Winning the defense challenge in an evasion competition with diversification
Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, and Konrad Rieck · 2020
Cited alongside, same era.
Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection, 2021
Luca Demetrio, Scott E. Coull, Battista Biggio, Giovanni Lagorio, Alessandro Armando, and Fabio Roli · 2021
Cited alongside, same era.
Maria Rigaki and Sebastian Garcia · 2023
Closest in time.
Defend against adversarial attacks in malware detection through attack space management
Liang Liu, Xinyu Kuang, Lin Liu, and Lei Zhang · 2024
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