LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning
Authors: 
Diandian Gu, Ziniu Hu, Shangchen Du, Yun MaAuthors Info & Claims
Diandian Gu, Ziniu Hu, Shangchen Du, Yun MaAuthors Info & ClaimsPages 2077 - 2080
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.
References
[1]
Sven Bugiel, Lucas Davi, Alexandra Dmitrienko, Thomas Fischer, Ahmad-Reza Sadeghi, and Bhargava Shastry. 2012. Towards Taming Privilege-Escalation Attacks on Android. (01 2012).
[2]
Erika Chin, Adrienne Porter Felt, Kate Greenwood, and David Wagner. 2011. Analyzing inter-application communication in Android. In International Conference on Mobile Systems, Applications, and Services. 239--252.
[3]
Michael Dietz, Shashi Shekhar, Yuliy Pisetsky, Anhei Shu, and S. Wallach Dan. 2011. QUIRE: Lightweight Provenance for Smart Phone Operating Systems. Dissertations & Theses - Gradworks (2011), 23--23.
[4]
Yu Feng, Saswat Anand, Isil Dillig, and Alex Aiken. 2014. Apposcopy: Semantic-based detection of android malware through static analysis. In Acm Sigsoft International Symposium on Foundations of Software Engineering .
[5]
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017a. Inductive Representation Learning on Large Graphs. (2017).
[6]
William L Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation Learning on Graphs: Methods and Applications. (2017).
[7]
Ziniu Hu, Yun Ma, Qiaozhu Mei, and Jian Tang. 2017. Roaming across the Castle Tunnels: an Empirical Study of Inter-App Navigation Behaviors of Android Users. CoRR, Vol. abs/1706.08274 (2017). arxiv: 1706.08274 http://arxiv.org/abs/1706.08274
[8]
B. Johnson, Yoonki Song, E. Murphy-Hill, and R. Bowdidge. 2013. Why don't software developers use static analysis tools to find bugs?. In International Conference on Software Engineering .
[9]
Li Li, Alexandre Bartel, Tegawendé F. Bissyandé, Jacques Klein, and Yves Le Traon. 2015. ApkCombiner: Combining Multiple Android Apps to Support Inter-App Analysis .
[10]
Yun Ma, Ziniu Hu, Yunxin Liu, Tao Xie, and Xuanzhe Liu. 2018. Aladdin: Automating Release of Deep-Link APIs on Android. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23--27, 2018 . 1469--1478. https://doi.org/10.1145/3178876.3186059
[11]
Yun Ma, Yangyang Huang, Ziniu Hu, Xusheng Xiao, and Xuanzhe Liu. 2019. Paladin: Automated Generation of Reproducible Test Cases for Android Apps. 99--104. https://doi.org/10.1145/3301293.3302363
[12]
Tomas Mikolov, Kai Chen, G.s Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. Proceedings of Workshop at ICLR, Vol. 2013 (01 2013).
[13]
Damien Octeau, Somesh Jha, Matthew Dering, Patrick McDaniel, Alexandre Bartel, Li Li, Jacques Klein, and Yves Le Traon. 2016. Combining Static Analysis with Probabilistic Models to Enable Market-Scale Android Inter-Component Analysis. In Proceedings of the 43rd ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) .
[14]
R. Socher, D. Chen, C. D. Manning, and A. Y. Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In International Conference on Neural Information Processing Systems .
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Information & Contributors
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Published In
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
- General Chairs:
- Wenwu Zhu,
- Dacheng Tao,
- Xueqi Cheng,
- Program Chairs:
- Peng Cui,
- Elke Rundensteiner,
- David Carmel,
- Qi He,
- Jeffrey Xu Yu
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Published: 03 November 2019
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- Open Project Fund of National Engineering Lab of Big Data System Software of China
- China Postdoctoral Science Foundation
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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management
November 3 - 7, 2019
Beijing, China
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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
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