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 ...
Aug 24, 2022 · Binary function similarity analysis evaluates the similarity of functions at the binary level to aid program analysis, which is popular in many ...
In this paper, we empirically demonstrate that the current state of the art in neural function boundary detection is vulnerable to both inadvertent and ...
ABSTRACT. Binary analyses based on deep neural networks (DNNs), or neural binary analyses (NBAs), have become a hotly researched topic in recent years.
Attacking Neural Binary Function Detection Joshua Bundt∗‖ § , Michael Davinroy∗ § , Ioannis Agadakos† , Alina Oprea∗ , William Robertson∗ ∗ Northeastern ...
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This paper empirically demonstrate that the current state of the art in neural function boundary detection is vulnerable to both inadvertent and deliberate ...
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 ...
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 2, 2023 · Machine Learning has pushed the boundaries of modern technology to new levels we previously thought were in the realm of science fiction.