Towards a Peer-to-Peer Federated Machine Learning Environment for Continuous Authentication.

The in-depth consideration of security aspects in modern web infrastructures has become essential to stay competitive. In this context, continuous authentication is a promising approach to prevent the misuse of digital identities. To this end, machine learning (ML) models are well suited to analyze user behavior and to detect anomalies, due to their ability to identify complex patterns and trends that usually cannot be reflected by static rule-based approaches. However, the training of powerful ML models requires large amounts of data, which are often not available within a single organization. Consequently, a federated training of these models by cooperating organizations offers a promising solution, but leads to concerns about coordination, regulations, and quality assurance. To tackle these challenges, we present an approach that combines three research areas: (1) the establishment of continuous user authentication based on (2) a ML model trained by an organized peer-to-peer federation involving different organizations that is underpinned by (3) federated data governance ensuring regulatory compliance and quality of resulting artefacts.