Reference : Combining Static Analysis with Probabilistic Models to Enable Market-Scale Android In...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/26631
Combining Static Analysis with Probabilistic Models to Enable Market-Scale Android Inter-component Analysis
English
Octeau, Damien []
Jha, Somesh []
Dering, Matthew []
McDaniel, Patrick []
Bartel, Alexandre []
Li, Li mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Le Traon, Yves mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Jan-2016
The 43rd Symposium on Principles of Programming Languages (POPL 2016)
Yes
No
International
The 43rd Symposium on Principles of Programming Languages (POPL 2016)
from 20-01-2016 to 23-01-2016
[en] Android ; Inter-component communication ; static analysis ; probabilistic program analysis
[en] Static analysis has been successfully used in many areas, from verifying mission-critical software to malware detection. Unfortunately, static analysis often produces false positives, which require significant manual effort to resolve. In this paper, we show how to overlay a probabilistic model, trained using domain knowledge, on top of static analysis results, in order to triage static analysis results. We apply this idea to analyzing mobile applications. Android application components can communicate with each other, both within single applications and between different applications. Unfortunately, techniques to statically infer Inter-Component Communication (ICC) yield many potential inter-component and inter-application links, most of which are false positives. At large scales, scrutinizing all potential links is simply not feasible. We therefore overlay a probabilistic model of ICC on top of static analysis results. Since computing the inter-component links is a prerequisite to inter-component analysis, we introduce a formalism for inferring ICC links based on set constraints. We design an efficient algorithm for performing link resolution. We compute all potential links in a corpus of 11,267 applications in 30 minutes and triage them using our probabilistic approach. We find that over 95.1% of all 636 million potential links are associated with probability values below 0.01 and are thus likely unfeasible links. Thus, it is possible to consider only a small subset of all links without significant loss of information. This work is the first significant step in making static inter-application analysis more tractable, even at large scales.
SnT
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/26631

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