text: Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads can mislead recommendation and search algorithms and further lead to bad user experience in App markets. In this paper, we investigate download fraud problem based on a company's App Market, which is one of the most popular Android App markets. We release a honeypot App on the App Market and purchase fake downloads from fraudster agents to track fraud activities in the wild. Based on our interaction with the fraudsters, we categorize download fraud activities into three types according to their intentions: boosting front end downloads, optimizing App search ranking, and enhancing user acquisition&retention rate. For the download fraud aimed at optimizing App search ranking, we select, evaluate, and validate several features in identifying fake downloads based on billions of download data. To get a comprehensive understanding of download fraud, we further gather stances of App marketers, fraudster agencies, and market operators on download fraud. The followed analysis and suggestions shed light on the ways to mitigate download fraud in App markets and other social platforms. To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.
  @cite_31: 
  @cite_14: An app market's vetting process is expected to be scalable and effective. However, today's vetting mechanisms are slow and less capable of catching new threats. In our research, we found that a more powerful solution can be found by exploiting the way Android malware is constructed and disseminated, which is typically through repackaging legitimate apps with similar malicious components. As a result, such attack payloads often stand out from those of the same repackaging origin and also show up in the apps not supposed to relate to each other. Based upon this observation, we developed a new technique, called MassVet, for vetting apps at a massive scale, without knowing what malware looks like and how it behaves. Unlike existing detection mechanisms, which often utilize heavyweight program analysis techniques, our approach simply compares a submitted app with all those already on a market, focusing on the difference between those sharing a similar UI structure (indicating a possible repackaging relation), and the commonality among those seemingly unrelated. Once public libraries and other legitimate code reuse are removed, such diff common program components become highly suspicious. In our research, we built this "Diff-Com" analysis on top of an efficient similarity comparison algorithm, which maps the salient features of an app's UI structure or a method's control-flow graph to a value for a fast comparison. We implemented MassVet over a stream processing engine and evaluated it nearly 1.2 million apps from 33 app markets around the world, the scale of Google Play. Our study shows that the technique can vet an app within 10 seconds at a low false detection rate. Also, it outperformed all 54 scanners in VirusTotal (NOD32, Symantec, McAfee, etc.) in terms of detection coverage, capturing over a hundred thousand malicious apps, including over 20 likely zero-day malware and those installed millions of times. A close look at these apps brings to light intriguing new observations e.g., Google's detection strategy and malware authors' countermoves that cause the mysterious disappearance and reappearance of some Google Play apps.
  @cite_22: Incentivized by monetary gain, some app developers launch fraudulent campaigns to boost their apps' rankings in the mobile app stores. They pay some service providers for boost services, which then organize large groups of collusive attackers to take fraudulent actions such as posting high app ratings or inflating apps' downloads. If not addressed timely, such attacks will increasingly damage the healthiness of app ecosystems. In this work, we propose a novel approach to identify attackers of collusive promotion groups in an app store. Our approach exploits the unusual ranking change patterns of apps to identify promoted apps, measures their pairwise similarity, forms targeted app clusters (TACs), and finally identifies the collusive group members. Our evaluation based on a dataset of Apple's China App store has demonstrated that our approach is able and scalable to report highly suspicious apps and reviewers. App stores may use our techniques to narrow down the suspicious lists for further investigation.
  @cite_32: 
  @cite_3: Recently emerged app markets provide a centralized paradigm for software distribution in smartphones. The difficulty of massively collecting app data has led to a lack a good understanding of app market dynamics. In this paper we seek to address this problem, through a detailed temporal analysis of Google Play, Google's app market. We perform the analysis on data that we collected daily from 160,000 apps, over a period of six months in 2012. We report often surprising results. For instance, at most 50 of the apps are updated in all categories, which significantly impacts the median price. The average price does not exhibit seasonal monthly trends and a changing price does not show any observable correlation with the download count. In addition, productive developers are not creating many popular apps, but a few developers control apps which dominate the total number of downloads. We discuss the research implications of such analytics on improving developer and user experiences, and detecting emerging threat vectors.
  @cite_15: With the rapid adoption of smartphones worldwide and the reliance on app marketplaces to discover new apps, these marketplaces are critical for connecting users with apps. And yet, the user reviews and ratings on these marketplaces may be strategically targeted by app developers. We investigate the use of crowdsourcing platforms to manipulate app reviews. We find that (i) apps targeted by crowdsourcing platforms are rated significantly higher on average than other apps; (ii) the reviews themselves arrive in bursts; (iii) app reviewers tend to repeat themselves by relying on some standard repeated text; and (iv) apps by the same developer tend to share a more similar language model: if one app has been targeted, it is likely that many of the other apps from the same developer have also been targeted.
  @cite_25: Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of globalanomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.
summary: Previous works have investigated various kinds of security issues in App markets. Chen @cite_14 and Rahman @cite_32 analyzed malware dissemination in Google Play. Zhu @cite_25 and Chen @cite_22 studied the suspicious Apps involved in search ranking in iOS App Store. @cite_15 delved crowdsourced spam reviews in both Google Play and iOS App Store. @cite_3 gave a longitudinal analysis of Apps in Google Play and provided suggestions on detecting search ranking fraud. According to @cite_31 , Google Play does not eliminate all fake downloads. Moreover, few previous works have investigated this problem either. It is mainly due to the lack of the ground truth of fraud activities. The data crawled from the front end, which has limited information, also hinders previous work for a comprehensive study on the download fraud. In this work, with server-side data and device vendor information as the ground truth, we could take a holistic approach to probe the download fraud in App market.