41 citations · 100 across the 5 of their papers we have counts for
5 papers
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…
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…
DeepOrigin: End-to-End Deep Learning for Detection of New Malware Families
Ilay Cordonsky, Ishai Rosenberg, Guillaume Sicard +1
In this paper, we present a novel method of differentiating known from previously unseen malware families. We utilize transfer learning by learning compact file representations tha…
DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
Ishai Rosenberg, Guillaume Sicard, Eli David
In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state…
Deep Self-Taught Learning for Handwritten Character Recognition
Frédéric Bastien, Yoshua Bengio, Arnaud Bergeron +14
Recent theoretical and empirical work in statistical machine learning has demonstrated the importance of learning algorithms for deep architectures, i.e., function classes obtained…