2014/10/28 by H. Güneş Kayacık, Mike Just, Kayacik, Hilmi Gunes +7
Computer Science · #Anomaly Detection Techniques and Applications #Context-Aware Activity Recognition Systems #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #User Authentication and Security Systems
paper · pdf · doi:10.48550/arxiv.1410.7743
openalex publication_date 2014/10/28 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
We propose a lightweight, and temporally and spatially aware user behaviour modelling technique for sensor-based authentication. Operating in the background, our data driven technique compares current behaviour with a user profile. If the behaviour deviates sufficiently from the established norm, actions such as explicit authentication can be triggered. To support a quick and lightweight deployment, our solution automatically switches from training mode to deployment mode when the user's behaviour is sufficiently learned. Furthermore, it allows the device to automatically determine a suitable detection threshold. We use our model to investigate practical aspects of sensor-based authentication by applying it to three publicly available data sets, computing expected times for training duration and behaviour drift. We also test our model with scenarios involving an attacker with varying knowledge and capabilities.