Abstract
In order to promote apps in mobile app stores, for malicious developers and users, manipulating average rating is a popular and feasible way. In this work, we propose a two-phase machine learning approach to detecting app rating manipulation attacks. In the first learning phase, we generate feature r
Highlights
The current mobile application markets such as iTunes App Store, Google Play, Amazon App Store, Windows Phone App Store, etc. provide a very convenient and efficient way to distribute mobile apps
From computer security’s perspective, those app stores become the targets of attackers for the two following reasons: 1) Users prefer to download apps with higher rating scores when they have multiple choices, because they usually consider higher average rating stands for higher quality; 2) App store providers show various app ranking
We proposed a machine learning based approach to detecting app rating manipulation
Summary
The current mobile application markets such as iTunes App Store, Google Play, Amazon App Store, Windows Phone App Store, etc. provide a very convenient and efficient way to distribute mobile apps. Provide a very convenient and efficient way to distribute mobile apps. The total app store revenue of Google Play and iTunes has exceeded 18.2 billions in the Q3 of 2018 alone [2]. All these numbers show that if developers release very popular apps, they could make a huge amount of profit from these apps. App store providers’ ranking algorithms [3] take reviewers’ ratings as an important factor. Some companies started the business to provide app promotion services and some of them even claim that they could keep the ranks that developers want for some periods, according to news articles [4]
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