English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The existence of native code in Android apps plays an important role intriggering inconspicuous propagation of secrets and circumventing malwaredetection. However, the state-of-the-art information-flow analysis tools forAndroid apps all have limited capabilities of analyzing native code. Due to thecomplexity of binary-level static analysis, most static analyzers choose tobuild conservative models for a selected portion of native code. Though therecent inter-language analysis improves the capability of tracking informationflow in native code, it is still far from attaining similar effectiveness ofthe state-of-the-art information-flow analyzers that focus on non-native Javamethods. To overcome the above constraints, we propose a new analysisframework, $\mu$Dep, to detect sensitive information flows of the Android appscontaining native code. In this framework, we combine a control-flow basedstatic binary analysis with a mutation-based dynamic analysis to model thetainting behaviors of native code in the apps. Based on the result of theanalyses, $\mu$Dep conducts a stub generation for the related native functionsto facilitate the state-of-the-art analyzer DroidSafe with fine-grainedtainting behavior summaries of native code. The experimental results show thatour framework is competitive on the accuracy, and effective in analyzing theinformation flows in real-world apps and malware compared with thestate-of-the-art inter-language static analysis.

$\mu$Dep: Mutation-based Dependency Generation for Precise Taint Analysis on Android Native Code