Analýza bezpečnostních hrozeb s ohledem na ochranu soukromí

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.