Cyber-attacks have become a severe threat for critical services in several domains, such as healthcare, manufacturing, telecom, energy, transportation, where the impact can be exceedingly high (e.g., in terms of service outages, private data breaches, intellectual property theft). Modern attacks are today very challenging, as they evolved into “Advanced Persistent Threats” (APTs). APT actors are typically cybercriminal or state-sponsored groups, which perform carefully-planned, stealthy attacks that span over a long period of time. A well-known example is the Stuxnet attack, which has been sabotaging Iran’s nuclear centrifuges since 2005, and was uncovered only in 2010.
FLEGREA is a research project that has been investigating new techniques for intrusion detection against APTs. In particular, the project investigated how to train and evaluate intrusion detection systems without forcing organizations to disclose their sensitive information, by emulating representative attacks using Generative AI and other techniques, and by integrating Federated Learning and Blockchain technology to make intrusion detection systems more secure and privacy-oriented.
All artifacts related to the research activities (datasets, prototypes) have been publicly released as open data, and reported on this website.