  User authentication and intrusion detection differ from standard
classification problems in that while we have data generated from legitimate
users, impostor or intrusion data is scarce or non-existent. We review existing
techniques for dealing with this problem and propose a novel alternative based
on a principled statistical decision-making view point. We examine the
technique on a toy problem and validate it on complex real-world data from an
RFID based access control system. The results indicate that it can
significantly outperform the classical world model approach. The method could
be more generally useful in other decision-making scenarios where there is a
lack of adversary data.
