41 citations · 140 across the 10 of their papers we have counts for
11 papers
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
Tzvika Shapira, David Berend, Ishai Rosenberg +3
The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training s…
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability
Ishai Rosenberg, Shai Meir, Jonathan Berrebi +3
In recent years, the topic of explainable machine learning (ML) has been extensively researched. Up until now, this research focused on regular ML users use-cases such as debugging…
FOOD: Fast Out-Of-Distribution Detector
Guy Amit, Moshe Levy, Ishai Rosenberg +2
Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in distribution inputs. However, out-of-dis…
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Ihai Rosenberg, Asaf Shabtai, Yuval Elovici +1
In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learni…
GIM: Gaussian Isolation Machines
Guy Amit, Ishai Rosenberg, Moshe Levy +3
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data. Samples from outside the distribution may be c…
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
Ishai Rosenberg, Guillaume Sicard, Eli David
Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state o…