We are thrilled to announce that, for the first time, the Czech Technical University in Prague is offering the "Introduction to Security" course as a Massive Open Online Course!
This blog covers my ongoing GSoC project 2024. It provides information about the current progress of my work, as well as my experiences and lessons learned along the way.
Stratosphere is happy to announce the accepted projects in the Google Summer of Code (GSoC) 2023!
After evaluating all the works sent by the candidates, at the end of the September 2022, the winner was selected! The award and money prize was given to Estelle Ruellan a master student of criminology from the Université de Montréal!
We are happy to announce that Slips will be presented at BlackHat USA 2022 Arsenal in Las Vegas on Thursday, August 11, from 1:00 pm to 2:30 pm: https://bit.ly/SlipsBHUSA22.
We share with everyone the need for an easier way of searching through these datasets to find the appropriate data needed for specific research. As a small step in this direction, we are introducing a new dataset index: https://mcfp.felk.cvut.cz/publicDatasets/datasets.html .
In this blog, we introduce our new dataset called the Android Mischief Dataset for the benefit of the security research community. It contains the network traffic from mobile devices infected with Android Remote Access Trojans. This blog describes the structure and the content of our dataset, its creation methodology, and links to download it.
This post is a continuation of the IoT-23 In Depth series based on the IoT-23 Dataset, the first dataset of malicious and benign IoT network traffic, that consists of 23 scenarios [1]. In this blog post we provide an analysis of Scenario 9 [2], CTU-IoT-Malware-Capture-60-1. This malware sample is called Gafgyt. This variant is an IoT malware family capable of different types of DDoS attacks and exploits vulnerabilities in other devices, such as routers, to expand its botnet which has been seen attacking gaming servers [3].
A couple of weeks ago, we released the IoT-23 Dataset, the first dataset of malicious and benign IoT network traffic, that consists of 23 scenarios. In this blog post we provide an analysis of Scenario 18, CTU-IoT-Malware-Capture-9-1. This malware sample is Hajime. We analysed the binary sample and the network traffic of this scenario.
