Bayesian Learned Models Can Detect Adversarial Malware For Free

Bayesian Learned Models Can Detect Adversarial Malware For Free

27 March 2024

Dang Quang Nguyen

Ehsan Abbasnejad

Damith C. Ranasinghe

Papers citing "Bayesian Learned Models Can Detect Adversarial Malware For Free"

18 / 18 papers shown

Title
SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection Richard E. Harang Ethan M. Rudd 56 101 0 14 Dec 2020
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection Deqiang Li Qianmu Li AAML 35 123 0 30 Jun 2020
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks Francesco Croce Maksym Andriushchenko Naman D. Singh Nicolas Flammarion Matthias Hein 71 101 0 23 Jun 2020
Quality of Uncertainty Quantification for Bayesian Neural Network Inference Jiayu Yao Weiwei Pan S. Ghosh Finale Doshi-Velez UQCV BDL 172 113 0 24 Jun 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 82 807 0 07 Feb 2019
Exploring Adversarial Examples in Malware Detection Octavian Suciu Scott E. Coull Jeffrey Johns AAML 58 192 0 18 Oct 2018
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 57 245 0 10 Aug 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 216 3,184 0 01 Feb 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 122 1,409 0 08 Dec 2017
Malware Detection by Eating a Whole EXE Edward Raff Jon Barker Jared Sylvester Robert Brandon Bryan Catanzaro Charles K. Nicholas 62 544 0 25 Oct 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 155 2,149 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 301 12,060 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 49 284 0 28 Apr 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 90 893 0 01 Mar 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 258 8,550 0 16 Aug 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 65 1,092 0 16 Aug 2016
Adversarial Perturbations Against Deep Neural Networks for Malware Classification Kathrin Grosse Nicolas Papernot Praveen Manoharan Michael Backes Patrick McDaniel AAML 64 418 0 14 Jun 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 271 19,045 0 20 Dec 2014