Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3358094 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
dynamic program analysis, inter-app page links, link prediction, transfer learning | Information retrieval,Computer science,Transfer of learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diandian Gu | 1 | 0 | 1.01 |
| Ziniu Hu | 2 | 90 | 11.15 |
| Shangchen Du | 3 | 0 | 0.34 |
| Yun Ma | 4 | 216 | 20.25 |