Aug 24, 2022 · We devise a simple, reproducible, and scalable black-box methodology for exploring the space of inadvertent attacks - instruction sequences that ...
Oct 16, 2023 · We devise a simple, reproducible, and scalable black-box methodology for exploring the space of inadvertent attacks – instruction sequences that ...
Oct 18, 2023 · ABSTRACT. Binary analyses based on deep neural networks (DNNs), or neural binary analyses (NBAs), have become a hotly researched topic in.
Black-box Attacks Against Neural Binary Function Detection
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Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally ...
Aug 2, 2023 · Machine Learning has pushed the boundaries of modern technology to new levels we previously thought were in the realm of science fiction.
May 4, 2022 · In this post we're going to cover the more common, and difficult, black box perspective. Here we only know what features are being extracted from each sample.
Aug 24, 2022 · We devise a simple, reproducible, and scalable black-box methodology for exploring the space of inadvertent attacks - instruction sequences that ...
Aug 26, 2022 · Re- cently, the learning-based BCSD methods have achieved great success, outperforming traditional BCSD in detection accuracy and efficiency.
In this work, we focus on defending against query-based black-box attacks, even when persistent attackers switch ac- count to evade detection. The fundamental ...
Existing black-box attacks on deep neural networks (DNNs) have largely focused on transferability, where an adversarial instance gen- erated for a locally ...