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Static Malware Detection & Subterfuge: Quantifying the Robustness of
  Machine Learning and Current Anti-Virus

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
ArXiv (abs)PDFHTML

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
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
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
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
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
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
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
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
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
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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