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.

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 ...