A measurement study of google play

By Nicolas Viennot, Edward Garcia, Jason Nieh. Measurement and Modeling of Computer Systems, 2014

403 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a58b4e6528a9c2698edd45ac1e16e017b82143b8

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Citations in the corpus

A Survey of App Store Analysis for Software Engineering (2017, methodology): “[226], 2014 SIGMETRICS 1,107,476 Bartel et al.”
SoK: Security Evaluation of Home-Based IoT Deployments (2019, methodology): “[41] showed, a large portion of the applications in the Google Play Store contain issues relating to permissions, programming errors, and information leakage.”
Rico: A Mobile App Dataset for Building Data-Driven Design Applications (2017, background): “, reviews, ratings) [2, 16], software engineering and security related information [38, 15], and design data [33, 4, 11].”
Machine learning aided Android malware classification (2017, background): “There have also been instances where malicious apps were successfully uploaded to Google Play [6].”
*droid (2016, background): “However, in recent work, researchers found that 14% of applications with fewer than 50,000 downloads contained at least one native library, whereas 70% of applications with more than 50 million downloads contained at least one native library [Viennot et al. 2014].”
Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps (2014, background): “Note that the above type of token leakage is very diﬀerent from the explicit token discoveries reported in a recent work [33].”
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection (2018, methodology): “• We evaluated the proposed manipulation methods of adversarial examples by using the same datasets that Drebin and MaMaDroid (5879 malware samples) used [1], [30].”
A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “With the support of the customized crawlers like PlayDrone [36], Android apps can be automatically browsed, retrieved, and downloaded into a repository for analysis [37, 38].”
Experimental Study with Real-world Data for Android App Security Analysis using Machine Learning (2015, methodology): “We particularly chose these two ML-approaches because they provide two canonical examples: (i) Drebin is unique in terms of using huge number (in order of hundreds of thousands) of features; (ii) DroidSIFT is unique in using a feature vector that is based on graph distance related to API dependance…”
Reliable Third-Party Library Detection in Android and its Security Applications (2016, result): “We opted for this approach, because prior studies [38, 46] have established that the Google Play Store is a “superstar” market in which a small percentage of the free applications (i.”
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (2016, methodology): “Our benign datasets consist of two sets of samples: (1) one, which we denote as oldbenign, includes 5,879 apps collected by PlayDrone [55] between April and November 2013, and published on the Internet Archive5 on August 7, 2014; and (2) another, newbenign, obtained by downloading the top 100 apps i”
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning (2018, neutral): “4 million Android apps available via the PlayDrone project [27] to find the links to 199,186 privacy policies.”