Statistical decision making for authentication and intrusion detection
Paper i proceeding, 2009

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. © 2009 IEEE.


Christos Dimitrakakis

Chalmers, Data- och informationsteknik, Datavetenskap

Aikaterini Mitrokotsa

Chalmers, Data- och informationsteknik, Nätverk och system

8th International Conference on Machine Learning and Applications, ICMLA 2009



Informations- och kommunikationsteknik



Data- och informationsvetenskap