TL;DR Using Tensorflow / Keras APIs to read and change Neural Network parameters. A few days ago I came across a paper called “Hacking Neural Networks” by Michael Kissner aka @Spellwrath.

TL;DR Using a Picoscope 2204A and its SDK to perform timing side channel attacks against a weak password checker implementation running in an Arduino. Code, screenshots and lessons learned! Last weekend we held the bi-annual Stratosphere hackathon.

Model stealing attacks have been successfully demonstrated in several domains. However, in the area of malware detection, there is no …

It is well known in the machine learning community that the performance of models degrades over time with the introduction of new data, getting worse in solving both new and old tasks. This is also true for security applications of machine learning, but there is less knowledge of how this happens, when, and why. In the particular case of malware classification, the increasing growth in the amount of malicious files forces the community to research machine learning models that use data more effectively. Inspired by the way humans learn, Continual Learning algorithms try to incrementally learn new tasks from data that keeps changing, without forgetting the tasks learned in the past. In this talk we will try to address the following questions: Is Continual Learning a suitable approach for security-related datasets and problems? Can Continual Learning methods learn new malware families effectively without forgetting old ones? What are the improvements in terms of speed and storage?

Reports on cyber espionage operations have been on the rise in the last decade. However, operations in Latin America are heavily under …

Generative Adversarial Networks (GANs) are a recent invention that shows impressive results in generating completely new images of …