Volgen
Ziv Katzir
Ziv Katzir
Ben-Gurion University, Department of Software and Information Systems Engineering
Geverifieerd e-mailadres voor post.bgu.ac.il
Titel
Geciteerd door
Geciteerd door
Jaar
Network identity clustering
Z Katzir
US Patent 7,882,217, 2011
1562011
System and method for assessing cybersecurity awareness
A Shabtai, R Puzis, L Rokach, L Orevi, G Malinsky, Z Katzir, R Bitton
US Patent 10,454,958, 2019
1012019
EduRank: Personalization in E-Learning using Collaborative Filtering
A Segal, Z Katzir, K Gal, G Shani, B Shapira
64*
Quantifying the resilience of machine learning classifiers used for cyber security
Z Katzir, Y Elovici
Expert Systems with Applications 92, 419-429, 2018
632018
Transfer learning for user action identication in mobile apps via encrypted trafc analysis
E Grolman, A Finkelshtein, R Puzis, A Shabtai, G Celniker, Z Katzir, ...
IEEE Intelligent Systems 33 (2), 40-53, 2018
402018
Detecting Adversarial Perturbations through Spatial Behavior in Activation Spaces
Z Katzir, Y Elovici
https://arxiv.org/abs/1811.09043, 2018
262018
Not all datasets are born equal: On heterogeneous tabular data and adversarial examples
Y Mathov, E Levy, Z Katzir, A Shabtai, Y Elovici
Knowledge-Based Systems 242, 108377, 2022
222022
Not all datasets are born equal: On heterogeneous data and adversarial examples
Y Mathov, E Levy, Z Katzir, A Shabtai, Y Elovici
arXiv preprint arXiv:2010.03180, 2020
192020
Method and system for context-aware data prioritization using a common scale and logical transactions
Z Katzir
US Patent 8,364,666, 2013
142013
Method and system for context-aware data prioritization
Z Katzir
US Patent App. 11/968,428, 2009
142009
System and method for generating data sets for learning to identify user actions
Z Katzir, G Celnicker, H Kovetz
US Patent 10,491,609, 2019
132019
Gradients cannot be tamed: Behind the impossible paradox of blocking targeted adversarial attacks
Z Katzir, Y Elovici
IEEE Transactions on Neural Networks and Learning Systems 32 (1), 128-138, 2020
102020
System and method for applying transfer learning to identification of user actions
R Puzis, A Shabtai, G Celniker, L Rosenfeld, Z Katzir, E Grolman
US Patent App. 15/911,223, 2018
62018
Who's afraid of adversarial transferability?
Z Katzir, Y Elovici
arXiv preprint arXiv:2105.00433, 2021
42021
Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn
Z Katzir, Y Elovici
https://arxiv.org/abs/1907.05718, 2019
42019
Adversarial robustness via stochastic regularization of neural activation sensitivity
G Fidel, R Bitton, Z Katzir, A Shabtai
arXiv preprint arXiv:2009.11349, 2020
22020
System and method for deanonymization of digital currency users
Z Katzir
US Patent App. 14/980,811, 2016
12016
System and method for assessing cybersecurity awareness
A Shabtai, R Puzis, L Rokach, L Orevi, G Malinsky, Z Katzir, R Bitton
US Patent 11,601,452, 2023
2023
Adversarial Machine Learning
Z Katzir, Y Elovici
Machine Learning for Data Science Handbook: Data Mining and Knowledge …, 2023
2023
System and method for generating data sets for learning to identify user actions
Z Katzir, G Celniker, H Kovetz
US Patent 11,303,652, 2022
2022
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–20