AndroidLeaks: Automatically Detecting Potential Privacy Leaks in Android Applications on a Large Scale

Gibler, Clint; Crussell, Jonathan; Erickson, Jeremy Lee; Chen, Hao

doi:10.1007/978-3-642-30921-2_17

Cited by 387 publications

(279 citation statements)

References 3 publications

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“…In [8], Batyuk et al proposed using static analysis for identifying security and privacy threats. AndroidLeaks [9], SCANDAL [10], and the approach presented in [11] are frameworks that detect privacy information leakage based on static analysis. Furthermore, in [12], the Android Application Sandbox (AAS) is proposed by Blasing et al AAS uses both static and dynamic analysis, where the static analysis part is based on matching 5 different patterns from decompiled code.…”

Section: Related Workmentioning

confidence: 99%

High accuracy android malware detection using ensemble learning

Yerima

Sezer

Muttik³

2015

IET Information Security

158

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Abstract-With over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor.Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

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Section: Related Workmentioning

confidence: 99%

High accuracy android malware detection using ensemble learning

Yerima

Sezer

Muttik³

2015

IET Information Security

158

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“…• We provide an improved version of DroidBench with 22 new apps for the assessment of tools which detect ICCbased privacy leaks.…”

Section: Introductionmentioning

confidence: 99%

IccTA: Detecting Inter-Component Privacy Leaks in Android Apps

Bartel

Bissyandé³

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

390

360

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Abstract-Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating wallpapers) and (2) use the device microphone (to change background following noise changes). With these permissions, the application could silently record user conversations and upload them remotely. To give more confidence about how Shake Them All actually processes what it records, it is necessary to build a precise analysis tool that tracks the flow of any sensitive data from its source point to any sink, especially if those are in different components.Since Android applications may leak private data carelessly or maliciously, we propose IccTA, a static taint analyzer to detect privacy leaks among components in Android applications. IccTA goes beyond state-of-the-art approaches by supporting intercomponent detection. By propagating context information among components, IccTA improves the precision of the analysis. IccTA outperforms existing tools on two benchmarks for ICC-leak detectors: DroidBench and ICC-Bench. Moreover, our approach detects 534 ICC leaks in 108 apps from MalGenome and 2,395 ICC leaks in 337 apps in a set of 15,000 Google Play apps.

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“…Androidleaks is a WALA based solution to detect privacy leakages in Android apps [20] [26]. It uses a system dependence graph to perform taint analysis.…”

Section: Static Privacy Leak Detectionmentioning

confidence: 99%

Analyzing Remote Server Locations for Personal Data Transfers in Mobile Apps

Eskandari

Kessler

Ahmad

et al. 2016

Proceedings on Privacy Enhancing Technologies

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Abstract:The prevalence of mobile devices and their capability to access high speed internet has transformed them into a portable pocket cloud interface. Being home to a wide range of users' personal data, mobile devices often use cloud servers for storage and processing. The sensitivity of a user's personal data demands adequate level of protection at the back-end servers. In this regard, the European Union Data Protection regulations (e.g., article 25.1) impose restriction on the locations of European users' personal data transfer. The matter of concern, however, is the enforcement of such regulations. The first step in this regard is to analyze mobile apps and identify the location of servers to which personal data is transferred. To this end, we design and implement an app analysis tool, PDTLoc (Personal Data Transfer Location Analyzer), to detect violation of the mentioned regulations. We analyze 1, 498 most popular apps in the EEA using PDTLoc to investigate the data recipient server locations. We found that 16.5% (242) of these apps transfer users' personal data to servers located at places outside Europe without being under the control of a data protection framework. Moreover, we inspect the privacy policies of the apps revealing that 51% of these apps do not provide any privacy policy while almost all of them contact the servers hosted outside Europe.

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AndroidLeaks: Automatically Detecting Potential Privacy Leaks in Android Applications on a Large Scale

Cited by 387 publications

References 3 publications

High accuracy android malware detection using ensemble learning

High accuracy android malware detection using ensemble learning

IccTA: Detecting Inter-Component Privacy Leaks in Android Apps

Analyzing Remote Server Locations for Personal Data Transfers in Mobile Apps

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