LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning
Proceedings of the 28th ACM International Conference on Information and Knowledge Management(2019)
摘要
Analyzing links among pages from different mobile apps is an important task of app analysis. Currently, most efforts of analyzing inter-app page links rely on static program analysis, which produces a lot of false positives, requiring significant manual effort to verify the links. To address the issue, in this paper, we propose LinkRadar, a data-driven approach to assisting the analysis of inter-app page links. Our key idea is to use dynamic program analysis to gather a set of actual inter-app page links, based on which we train a model to predict whether there exist links among pages from different apps to help verify the results of static program analysis. The challenge is that inter-app page links are hard to be triggered by dynamic program analysis, making it difficult to collect enough inter-app page links to train the model. Considering the similarity between intra-app page links and inter-app page links, we use transfer learning to deal with the data scarcity problem. Evaluation results show that LinkRadar is able to infer the inter-app page links with high accuracy.
更多查看译文
关键词
dynamic program analysis, inter-app page links, link prediction, transfer learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要