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A Framework for Cluster and Classifier Evaluation in the Absence of
  Reference Labels

A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels

23 September 2021
R. Joyce
Edward Raff
Charles K. Nicholas
ArXivPDFHTML

Papers citing "A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels"

14 / 14 papers shown
Title
Living off the Analyst: Harvesting Features from Yara Rules for Malware
  Detection
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Siddhant Gupta
Fred Lu
Andrew Barlow
Edward Raff
Francis Ferraro
Cynthia Matuszek
Charles K. Nicholas
James Holt
61
0
0
27 Nov 2024
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
17
2
0
07 May 2024
Understanding the Process of Data Labeling in Cybersecurity
Understanding the Process of Data Labeling in Cybersecurity
Tobias Braun
Irdin Pekaric
Giovanni Apruzzese
20
4
0
28 Nov 2023
High-resolution Image-based Malware Classification using Multiple
  Instance Learning
High-resolution Image-based Malware Classification using Multiple Instance Learning
Tim Peters
H. Farhat
8
0
0
21 Nov 2023
Validation of the Practicability of Logical Assessment Formula for
  Evaluations with Inaccurate Ground-Truth Labels
Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels
Yongquan Yang
Hong Bu
8
0
0
06 Jul 2023
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion
  Detection
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection
Giovanni Apruzzese
P. Laskov
J. Schneider
25
24
0
30 Apr 2023
Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric
  Perspective
Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective
Yuhang Zhang
Weihong Deng
Liang Zheng
OODD
17
4
0
16 Feb 2023
"Real Attackers Don't Compute Gradients": Bridging the Gap Between
  Adversarial ML Research and Practice
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese
Hyrum S. Anderson
Savino Dambra
D. Freeman
Fabio Pierazzi
Kevin A. Roundy
AAML
24
75
0
29 Dec 2022
Efficient Malware Analysis Using Metric Embeddings
Efficient Malware Analysis Using Metric Embeddings
Ethan M. Rudd
David B. Krisiloff
Scott E. Coull
Daniel Olszewski
Edward Raff
James Holt
AAML
13
6
0
05 Dec 2022
Firenze: Model Evaluation Using Weak Signals
Firenze: Model Evaluation Using Weak Signals
Bhavna Soman
A. Torkamani
Michael J. Morais
Jeffrey Bickford
Baris Coskun
17
2
0
02 Jul 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
22
53
0
09 Mar 2022
MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels
MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels
R. Joyce
Dev Amlani
B. Hamilton
Edward Raff
16
21
0
29 Nov 2021
Logical Assessment Formula and Its Principles for Evaluations with
  Inaccurate Ground-Truth Labels
Logical Assessment Formula and Its Principles for Evaluations with Inaccurate Ground-Truth Labels
Yongquan Yang
16
3
0
22 Oct 2021
Leveraging Uncertainty for Improved Static Malware Detection Under
  Extreme False Positive Constraints
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints
A. Nguyen
Edward Raff
Charles K. Nicholas
James Holt
16
21
0
09 Aug 2021
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