Using A Personalized Anomaly Detection Approach with Machine Learning to Detect Stolen Phones

摘要

We devise an anomaly detection system that detects stolen phones. In this system, we use a mining algorithm to extract sequential patterns from a user's past behavior to construct a personalized model. We then put forward scoring functions and threshold setting strategies to detect stealing events. We evaluate our approach with a data set from the MIT Reality Mining project. Experimental results indicate that our approach can detect 87% of simulated stealing events with an average false positive rate of 0.9%.