Privacy-Preserving Intrusion Detection

Kurzfassung

The growing complexity of cyberattacks has made intrusion detection systems (IDS) an indispensable component of modern defense strategies. In particular, deep learning-based IDS promise powerful detection capabilities, but their effectiveness relies on access to large and diverse datasets. Such datasets, however, typically contain highly sensitive information about internal network structures, communication patterns, and service usage, which organizations are unwilling or unable to share. This creates a paradox: stronger defenses require collaboration, yet collaboration is hindered by confidentiality and trust concerns.