Soft Authentication with Low-Cost Signatures

As mobile context-aware services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities for applications such as user authentication, adaptive assist technologies and remote elder-care monitoring. Existing approaches have limited applicability as they regularly poll power-hungry sensors (e.g., accelerometer, GPS) reducing the availability of devices to perform anomaly detection. This paper present SALCS (Soft Authentication with Low-Cost Signatures), an approach for anomaly detection on a user's routine comprised of a collection of anomaly detection techniques utilizing soft-sensor data (e.g., call-logs, messages) and radio channel information (e.g., GSM cell IDs), all of which are available as part of a phone's routine usage. Using these information sources we model aspects of a person's routine, such as movement, messaging and conversation patterns. We present extensive evaluations of the individual anomaly detection techniques, compare the collection SALCS to an existing power-hungry approach showing SALCS has a 7.6% higher detection rate and gives 5x better coverage throughout the day.