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  4. Cited By
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"

15 / 15 papers shown
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Chang Liu
Rebecca Saul
Yihao Sun
Edward Raff
Maya Fuchs
Townsend Southard Pantano
James Holt
Kristopher K. Micinski
262
16
0
07 May 2024
High-resolution Image-based Malware Classification using Multiple
  Instance Learning
High-resolution Image-based Malware Classification using Multiple Instance Learning
Tim Peters
H. Farhat
279
0
0
21 Nov 2023
Instance Attack:An Explanation-based Vulnerability Analysis Framework
  Against DNNs for Malware Detection
Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware DetectionPeerJ Computer Science (PeerJ CS), 2022
Ruijin Sun
Shize Guo
Jinhong Guo
Changyou Xing
Luming Yang
Xi Guo
Zhisong Pan
AAML
340
2
0
06 Sep 2022
Stealing and Evading Malware Classifiers and Antivirus at Low False
  Positive Conditions
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsComputers & security (Comput. Secur.), 2022
M. Rigaki
Sebastian Garcia
AAML
343
12
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 SystemsIEEE Transactions on Network and Service Management (IEEE TNSM), 2022
Giovanni Apruzzese
Luca Pajola
Mauro Conti
270
84
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-ArtComputers & security (CS), 2021
Xiang Ling
Lingfei Wu
Jiangyu Zhang
Zhenqing Qu
Wei Deng
...
Chunming Wu
S. Ji
Tianyue Luo
Jingzheng Wu
Yanjun Wu
AAML
653
106
0
23 Dec 2021
MALIGN: Explainable Static Raw-byte Based Malware Family Classification
  using Sequence Alignment
MALIGN: Explainable Static Raw-byte Based Malware Family Classification using Sequence AlignmentComputers & security (CS), 2021
Shoumik Saha
Sadia Afroz
A. Rahman
458
10
0
28 Nov 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
160
2
0
22 Nov 2021
A Survey on Adversarial Attacks for Malware Analysis
A Survey on Adversarial Attacks for Malware AnalysisIEEE Access (IEEE Access), 2021
Kshitiz Aryal
Maanak Gupta
Mahmoud Abdelsalam
AAML
385
74
0
16 Nov 2021
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
John Boutsikas
M. Eren
Charles K. Varga
Edward Raff
Cynthia Matuszek
Charles K. Nicholas
119
6
0
15 Jun 2021
Classifying Sequences of Extreme Length with Constant Memory Applied to
  Malware Detection
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionAAAI Conference on Artificial Intelligence (AAAI), 2020
Edward Raff
William Fleshman
Richard Zak
Hyrum S. Anderson
Bobby Filar
Mark McLean
AAML
209
70
0
17 Dec 2020
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of
  Machine Learning-Based Malware Detection
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection
Robert A. Bridges
Sean Oesch
Miki E. Verma
Michael D. Iannacone
Kelly M. T. Huffer
...
Daniel Scofield
Craig Miles
Thomas Plummer
Mark Daniell
Anne M. Tall
207
6
0
16 Dec 2020
Getting Passive Aggressive About False Positives: Patching Deployed
  Malware Detectors
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors
Edward Raff
Bobby Filar
James Holt
216
9
0
22 Oct 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
386
69
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
627
29
0
06 Mar 2020
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