Phone number recycling (PNR) refers to the event wherein a mobile operator collects a disconnected number and reassigns it to a new owner. It has posed a threat to the reliability of the existing authentication solution for e-commerce platforms. Specifically

a new owner of a reassigned number can access the application account that the number is associated with

and may perform fraudulent activities. Existing solutions that employ a reassigned number database from mobile operators are costly for e-commerce platforms with large-scale users. Thus

alternative solutions that depend on only the information of the applications are imperative. In this work

we study the problem of detecting accounts that have been compromised owing to the reassignment of phone numbers. Our analysis on Meituan’s real-world dataset shows that compromised accounts have unique statistical features and temporal patterns. Based on the observations

we propose a novel model called Temporal pattern and Statistical feature Fusion model (TSF) to tackle the problem

which integrates a Temporal Pattern Encoder and a Statistical Feature Encoder to capture behavioral evolutionary interaction and significant operation features. Extensive experiments on the Meituan and IEEE-CIS datasets show that TSF significantly outperforms the compared baselines

which demonstrates its effectiveness in detecting compromised accounts due to reassigned numbers.