Using a Personalized Machine Learning Approach to Detect Stolen Phones
With the increasing number of smartphone penetration, mining smartphone data that make smartphone smarter became a top research area, there are a lot of event data which we can use to predict behavior or detect anomalies. The privacy disclosure caused by stolen or lost phones becomes an increasingly difficult problem that cannot be ignored. So we design an anomaly detection system by mining patterns to detect stolen phones. We use a pattern mining algorithm to abstract patterns from user past behavior, then construct a personalized model and use a scoring function and threshold setting strategy to detect stolen events. Moreover, we apply our system to a data set from MIT Real Mining Project. Experimental results show that our system can detect 87% stolen events with 0.009 false positive rate on the average.