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1806.04773
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Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
12 June 2018
William Fleshman
Edward Raff
Richard Zak
Mark McLean
Charles K. Nicholas
AAML
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Papers citing
"Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus"
9 / 9 papers shown
Title
High-resolution Image-based Malware Classification using Multiple Instance Learning
Tim Peters
H. Farhat
42
0
0
21 Nov 2023
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions
M. Rigaki
Sebastian Garcia
AAML
74
11
0
13 Apr 2022
The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
Giovanni Apruzzese
Luca Pajola
Mauro Conti
81
56
0
09 Mar 2022
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Xiang Ling
Lingfei Wu
Jiangyu Zhang
Zhenqing Qu
Wei Deng
...
Chunming Wu
S. Ji
Tianyue Luo
Jingzheng Wu
Yanjun Wu
AAML
145
83
0
23 Dec 2021
A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries
P. Dasgupta
Zachary Osman
AAML
64
2
0
22 Nov 2021
A Survey on Adversarial Attacks for Malware Analysis
Kshitiz Aryal
Maanak Gupta
Mahmoud Abdelsalam
AAML
106
53
0
16 Nov 2021
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection
Edward Raff
William Fleshman
Richard Zak
Hyrum S. Anderson
Bobby Filar
Mark McLean
AAML
59
58
0
17 Dec 2020
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification
Edward Raff
Charles K. Nicholas
AAML
72
57
0
15 Jun 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
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