Analýza bezpečnostních hrozeb s ohledem na ochranu soukromí

Privacy-respecting Explainable Assessment and Collection of Threats

grant

Indicators of Compromise, Federated Learning, Privacy preserving, Anomaly detection, Cybersecurity

This project aims to research and develop a cybersecurity threat detection system that emphasizes the collection of Indicators of Compromise (IoCs) from anomaly detection systems placed in multiple customer networks, while ensuring the privacy of the individuals who are monitored by those systems. The primary goal is to increase the cybersecurity of end users through enhanced threat detection capabilities of anomaly detection systems based on the cooperation of those systems. The challenge, however, is to address the potential privacy concerns associated with collecting and analyzing sensitive data such as IP addresses, login credentials, and activity patterns. Such information can potentially be misused and lead to privacy violations. Therefore, the project aims to protect sensitive data and provide end users with a robust privacy guarantee to encourage them to share detected security events enabling to determine IoCs for external analysis and increasing threat detection.

Ryšavý Ondřej, doc. Ing., Ph.D. (UIFS FIT VUT) , research leader
Matoušek Petr, doc. Ing., Ph.D., M.A. (UIFS FIT VUT) , team leader
Burgetová Ivana, Ing., Ph.D. (UIFS FIT VUT)
Korvas Václav, Bc. (FIT VUT)
Mutua Nelson Makau, MSc. (UIFS FIT VUT)
Ondryáš Ondřej, Bc. (UIFS FIT VUT)
Polčák Libor, Ing., Ph.D. (UIFS FIT VUT)
Rychlý Marek, RNDr., Ph.D. (UIFS FIT VUT)