To investigate the status quo of SEAndroid policy customization, we propose SEPAL, a universal tool to automatically retrieve and examine the customized policy rules. SEPAL applies the NLP technique and employs and trains a wide&deep model to quickly and precisely predict whether one rule is unregulated or not.Our evaluation shows SEPAL is effective, practical and scalable. We verify SEPAL outperforms the state of the art approach (i.e., EASEAndroid) by 15% accuracy rate on average. In our experiments, SEPAL successfully identifies 7,111 unregulated policy rules with a low false positive rate from 595,236 customized rules (extracted from 774 Android firmware images of 72 manufacturers). We further discover the policy customization problem is getting worse in newer Andro
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