• The Hitchhiker's Guide to Network Security Measurement

    Keynote of day 2 in TMA 2023 conference.

  • deck

  • deck

  • ML for Propaganda Detection

  • AI and human rights

  • Modelling Computational Propaganda. A Proposal

  • Aposemat Project Update April 2019

  • Emergency VPN: Analyzing mobile network traffic to detect digital threats

  • IoT Lab in Stratosphere

  • Spy vs. Spy: A modern study of mic bugs operation and detection. BsidesBud

    HITB GSEC 2017 presentation of a research about spying microphones and how to detect them. TL;DR: Don't be fooled. Audio eavesdropping is a real threat. We built a free software tool to detect and locate hidden mic bugs.

  • Spy vs. Spy: A modern study of mic bugs operation and detection. 34C3

    HITB GSEC 2017 presentation of a research about spying microphones and how to detect them. TL;DR: Don't be fooled. Audio eavesdropping is a real threat. We built a free software tool to detect and locate hidden mic bugs.

  • Machine Learning en Seguridad Informática: Funciona?

  • Cybersecurity for Civil Society

  • Spy vs. Spy: A modern study of mic bugs operation and detection

    HITB GSEC 2017 presentation of a research about spying microphones and how to detect them. TL;DR: Don't be fooled. Audio eavesdropping is a real threat. We built a free software tool to detect and locate hidden mic bugs.

  • Can AI help Security?

  • Detecting the Behavioral Relationships of Malware Connections

    We still have problems to solve when it comes to detecting malware in the network. If the malware is new, there are not signatures, no IoCs, no threat information. If you have thousands of hosts you can not even analyze the payloads, or you don't have payloads and have to resort to NetFlows. In this limited context is where we developed a new idea to detect the behavioral patterns of how a computer works in the network by analyzing its communications as a cyclic graph. Our technique applies new concepts to reduce the information being analyzed while retaining and graphing the major features. We test our concept on dozens of Normal and Malware traffic, which gives significance to the work. The takeaway is: the way you use your computer leaves traces in the network, and those traces can be used to detect when you are infected.

  • Machine Learning, Security and the Stratosphere Project

    Stratosphere IPS. The free IPS for NGOs.

  • Stratosphere IPS. The free machine learning malware detection for the community

    Stratosphere IPS. The free IPS for NGOs.

  • Detecting the Cyclic Behavior of Malware with graph theory. The Cerber ransomware case

    We still have problems to solve when it comes to detecting malware in the network. If the malware is new, there are not signatures, no IoCs, no threat information. If you have thousands of hosts you can not even analyze the payloads, or you don't have payloads and have to resort to NetFlows. In this limited context is where we developed a new idea to detect the behavioral patterns of how a computer works in the network by analyzing its communications as a cyclic graph. Our technique applies new concepts to reduce the information being analyzed while retaining and graphing the major features. We test our concept on dozens of Normal and Malware traffic, which gives significance to the work. In particular we will show a demo with the analysis of a Cerber ransomware capture. The takeaway is: the way you use your computer leaves traces in the network, and those traces can be used to detect when you are infected.

  • Stratosphere Project: Free Software Machine Learning to protect NGOs

    Protecting NGOs with Stratosphere Project

  • Detecting the Behavioral Relationships of Malware Connections

  • Stratosphere IPS. Free Software Machine Learning for the Community.

    Presentation about a Linux Botnet analysis for www.security-session.cz

  • Malware Detection in the Network. Behavioral Analysis with Machine Learning

    Stratosphere Project

  • Malware behavior in the Network. A deep analysis with Machine Learning

    Presentation about how the Malware behaves in the network and how to detect it using Machine Learning

  • Stratosphere IPS: Machine learning and behavioral patterns to detect malicious behaviors in network traffic

    Protecting NGOs with Stratosphere Project

  • Global Network Defense Strategy, the Project

  • Stratosphere Project - Basics

    Protecting NGOs with Stratosphere Project

  • Robots against robots: How a Machine Learning IDS detected a novel Linux Botnet

    Presentation about a Linux Botnet analysis for www.security-session.cz

  • Global Network Defense Strategy

  • Stratosphere Project for NGOs

    Protecting NGOs with Stratosphere Project