RacketStore: Measurements of ASO Deception in Google Play via Mobile and App Usage
Nestor Hernandez, Ruben Recabarren, Bogdan Carbunar, Syed Ishtiaque, Ahmed

TL;DR
This paper introduces RacketStore, a data collection platform that analyzes app installation and review behaviors to detect fraudulent ASO activities, achieving high accuracy in identifying paid installs and fake reviews.
Contribution
The study provides the first large-scale measurement of ASO deception tactics and develops machine learning models that effectively detect fraudulent app promotion activities.
Findings
High detection accuracy for paid installs and fake reviews.
Significant behavioral differences between ASO providers and regular users.
Insights into evasion costs for detection methods.
Abstract
Online app search optimization (ASO) platforms that provide bulk installs and fake reviews for paying app developers in order to fraudulently boost their search rank in app stores, were shown to employ diverse and complex strategies that successfully evade state-of-the-art detection methods. In this paper we introduce RacketStore, a platform to collect data from Android devices of participating ASO providers and regular users, on their interactions with apps which they install from the Google Play Store. We present measurements from a study of 943 installs of RacketStore on 803 unique devices controlled by ASO providers and regular users, that consists of 58,362,249 data snapshots collected from these devices, the 12,341 apps installed on them and their 110,511,637 Google Play reviews. We reveal significant differences between ASO providers and regular users in terms of the number and…
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Taxonomy
TopicsSpam and Phishing Detection · Advanced Malware Detection Techniques · Internet Traffic Analysis and Secure E-voting
