41 citations · 67 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
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…
Defense Methods Against Adversarial Examples for Recurrent Neural Networks
Ishai Rosenberg, Asaf Shabtai, Yuval Elovici +1
Adversarial examples are known to mislead deep learning models to incorrectly classify them, even in domains where such models achieve state-of-the-art performance. Until recently,…
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…
Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers
Ishai Rosenberg, Asaf Shabtai, Yuval Elovici +1
In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers. We generate adversarial examples by modifying the…
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…