Millions of mobile apps have been available through various app markets. Although most app markets have enforced a number of automated or even manual mechanisms to vet each app before it is released to the market, thousands of low-quality apps still exist in different markets, some of which violate the explicitly specified market policies. In order to identify these violations accurately and timely, we resort to user comments, which can form an immediate feedback for app market maintainers, to identify undesired behaviors that violate market policies, including security-related user concerns. Specifically, we present the first large-scale study to detect and characterize the correlations between user comments and market policies. First, we propose CHAMP, an approach that adopts text mining and natural language processing (NLP) techniques to extract semantic rules through a semi-automated process, and classifies comments into 26 pre-defined types of undesired behaviors that violate market policies. Our evaluation on real-world user comments shows that it achieves both high precision and recall (>0.9) in classifying comments for undesired behaviors. Then, we curate a large-scale comment dataset (over 3 million user comments) from apps in Google Play and 8 popular alternative Android app markets, and apply CHAMP to understand the characteristics of undesired behavior comments in the wild. The results confirm our speculation that user comments can be used to pinpoint suspicious apps that violate policies declared by app markets. The study also reveals that policy violations are widespread in many app markets despite their extensive vetting efforts. CHAMP can be a whistle blower that assigns policy-violation scores and identifies most informative comments for apps.

In this work, we are focusing on a new and yet uncovered way for malicious apps to gain profit. They claim to be dating apps. However, their sole purpose is to lure users into purchasing premium/VIP services to start conversations with other (likely fake female) accounts in the app. We call these apps as fraudulent dating apps. This paper performs a systematic study to understand the whole ecosystem of fraudulent dating apps. Specifically, we have proposed a three-phase method to detect them and subsequently comprehend their characteristics via analyzing the existing account profiles. Our observation reveals that most of the accounts are not managed by real persons, but by chatbots based on predefined conversation templates. We also analyze the business model of these apps and reveal that multiple parties are actually involved in the ecosystem, including producers who develop apps, publishers who publish apps to gain profit, and the distribution network that is responsible for distributing apps to end users. Finally, we analyze the impact of them to users (i.e., victims) and estimate the overall revenue. Our work is the first systematic study on fraudulent dating apps, and the results demonstrate the urge for a solution to protect users.

Mobile advertising has become the most popular monetizing way in the Android app ecosystem. Offerwall, as a new form of mobile ads, has been widely adopted by apps, and a number of ad networks have provided such services. Although new to the ecosystem, offerwall ads have been criticized for being aggressive, and the contents disseminated are prone to security issues. However, to date, our community has not proposed any studies to dissect such issues related to offerwall ads. To this end, we present the first work to fill this gap. Specifically, we first develop a robust approach to identify apps that have embedded with offerwall ads. Then, we apply the tool to 10K apps and experimentally discover 312 offerwall apps. We go one step further to characterize them from several aspects, including security issues. Our observation reveals that offerwall ads could indeed be manipulated by hackers to fulfill malicious purposes.

Domain squatting, the adversarial tactic where attackers register domain names that mimic popular ones, has been observed for decades. However, there has been growing anecdotal evidence that this style of attack has spread to other domains. In this paper, we explore the presence of squatting attacks in the mobile app ecosystem. In “App Squatting”, attackers release apps with identifiers (e.g., app name or package name) that are confusingly similar to those of popular apps or well-known Internet brands. This paper presents the first in-depth measurement study of app squatting showing its prevalence and implications. We first identify 11 common deformation approaches of app squatters and propose “AppCrazy”, a tool for automatically generating variations of app identifiers. We have applied AppCrazy to the top-500 most popular apps in Google Play, generating 224,322 deformation keywords which we then use to test for app squatters on popular markets. Through this, we confirm the scale of the problem, identifying 10,553 squatting apps (an average of over 20 squatting apps for each legitimate one). Our investigation reveals that more than 51% of the squatting apps are malicious, with some being extremely popular (up to 10 million downloads). Meanwhile, we also find that mobile app markets have not been successful in identifying and eliminating squatting apps. Our findings demonstrate the urgency to identify and prevent app squatting abuses. To this end, we have publicly released all the identified squatting apps, as well as our tool AppCrazy.

Have you ever thought of earning profits from the apps that you are using on your mobile device? It is actually achievable thanks to many so-called money-making apps, which pay app users to complete tasks such as installing another app or clicking an advertisement. To the best of our knowledge, no existing studies have investigated the characteristics of moneymaking apps. To this end, we conduct the first exploratory study to understand the features and implications of money-making apps. We first propose a semi-automated approach aiming to harvest money-making apps from Google Play and alternative app markets. Then we create a taxonomy to classify them into five categories and perform an empirical study from different aspects. Our study reveals several interesting observations: (1) moneymaking apps have become the target of malicious developers, as we found many of them expose mobile users to serious privacy and security risks. Roughly 26% of the studied apps are potentially malicious. (2) these apps have attracted millions of users, however, many users complain that they are cheated by these apps. We also revealed that ranking fraud techniques are widely used in these apps to promote the ranking of apps inside app markets. (3) these apps usually spread inappropriate and malicious contents, while unsuspicious users could get infected. Our study demonstrates the emergency for detecting and regulating this kind of apps and protect mobile users.