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.
Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system. In this blog we introduce version 0.9.0, with support of peer-to-peer threat intelligence sharing.
Privacy-preserving DNS protocols like DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ) have been around since 2014 but they have only recently been brought to the attention of the general public following Firefox’s announcement to make DoH a default.
In the last couple of months we have been busy continuing with the development of Slips, our behaviour machine learning system. And finally we published version 0.8, with crazy amount of features! You can download it from here https://github.com/stratosphereips/StratosphereLinuxIPS. And read the documentation here https://stratospherelinuxips.readthedocs.io/en/develop/.
The “Attacking Active Directory Game” is part of a project where our researcher Ondrej Lukas developed a way to create fake Active Directory (AD) users as honey-tokens to detect attacks. His machine learning model was trained in real AD structures and can create a complete new fake user that is strategically placed in the structure of a company.
The Stratosphere Linux IPs, for short Slips, is a free software intrusion prevention system that uses machine learning. Slips allows analysts to quickly sift through large network captures as well as real live traffic, highlighting what is important to analyze. The analysis we do as part of the Emergency VPN service at Civilsphere relies heavily on Slips.
Machine learning systems are now ubiquitous and work well in several applications, but it is still relatively unexplored how much information they can leak. This blog post explores the most recent techniques that cause ML models to leak private data, an overview of the most important attacks, and why this type of attacks are possible in the first place.
Last week I had the opportunity to attend the 1st Transylvanian Deep Learning Summer School (TMLSS). It took place in Cluj, Romania and its main focus was Deep Learning and Reinforcement Learning. Here is the link to website that includes the programme and the full list of lecturers. (link)
