A Knowledge Graph based Approach for Apps Permission Recommendation
Huwei Zhang, Zhiyong Feng, Jianmao Xiao, Zhixiong Ye, Guodong Fan, Shizhan Chen, Xiao Xue · 2022
The incompleteness of android documentation causes the lack of permission semantics, which in turn leads to permission misuse and threatens android system security. Previous studies have used API information to supplement permission semantics and recommended permissions, but they still lack sufficient contextual information, e.g. category and user review grades. To solve this problem, a novel Knowledge Graph based Convolutional Propagation Model (KGCP) is proposed for apps permission recommendation. In KGCP, we construct a knowledge graph (KG) to model the contextual information corresponding to apps and permissions. In order to regularize the representations of items, KGCP utilizes KG embedding technique to preserve its intrinsic structure while embedding entities and relationships into a continuous vector space. Focused on apps permission recommendation, KGCP learns the representation of entity through graph convolutional networks, which recursively aggregates information about neighbors in KG to mine potential preferences for app entities over permission entities. Experimental results show that KGCP improves by 16.7% over state-of-the-art apps permission recommendation methods, thus helps developers find the suitable permissions faster and more accurately.