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.
For the black markets investigation, several works @cite_7 @cite_23 @cite_37 @cite_17 have probed the crowdsourcing websites and devised machine learning approaches to detect crowdturfing campaigns and crowd workers. Other works like @cite_4 inspected the transactions over trading App reviews and @cite_2 investigated the crowd fraud in Internet advertisement. However, seldom previous work has studied the black markets targeting download fraud. Like @cite_8 @cite_5 @cite_24 , we launch a honeypot App in App market to acquire reliable ground truth of download fraud activities. Moreover, we infiltrate into the black market and reap useful information from fraudsters to help our analysis.
@cite_37 @cite_4 @cite_7 @cite_8 @cite_24 @cite_23 @cite_2 @cite_5 @cite_17
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The rise of crowdsourcing brings new types of malpractices in Internet advertising. One can easily hire web workers through malicious crowdsourcing platforms to attack other advertisers. Such human generated crowd frauds are hard to detect by conventional fraud detection methods. In this paper, we carefully examine the characteristics of the group behaviors of crowd fraud and identify three persistent patterns, which are moderateness, synchronicity and dispersivity. Then we propose an effective crowd fraud detection method for search engine advertising based on these patterns, which consists of a constructing stage, a clustering stage and a filtering stage. At the constructing stage, we remove irrelevant data and reorganize the click logs into a surfer-advertiser inverted list; At the clustering stage, we define the sync-similarity between surfers' click histories and transform the coalition detection to a clustering problem, solved by a nonparametric algorithm; and finally we build a dispersity filter to remove false alarm clusters. The nonparametric nature of our method ensures that we can find an unbounded number of coalitions with nearly no human interaction. We also provide a parallel solution to make the method scalable to Web data and conduct extensive experiments. The empirical results demonstrate that our method is accurate and scalable. As human computation on crowdsourcing systems has become popular and powerful for performing tasks, malicious users have started misusing these systems by posting malicious tasks, propagating manipulated contents, and targeting popular web services such as online social networks and search engines. Recently, these malicious users moved to Fiverr, a fast-growing micro-task marketplace, where workers can post crowdturfing tasks (i.e., astroturfing campaigns run by crowd workers) and malicious customers can purchase those tasks for only $5. In this paper, we present a comprehensive analysis of Fiverr. First, we identify the most popular types of crowdturfing tasks found in this marketplace and conduct case studies for these crowdturfing tasks. Then, we build crowdturfing task detection classifiers to filter these tasks and prevent them from becoming active in the marketplace. Our experimental results show that the proposed classification approach effectively detects crowdturfing tasks, achieving 97.35 accuracy. Finally, we analyze the real world impact of crowdturfing tasks by purchasing active Fiverr tasks and quantifying their impact on a target site. As part of this analysis, we show that current security systems inadequately detect crowdsourced manipulation, which confirms the necessity of our proposed crowdturfing task detection approach. Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80 of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2 higher accuracy (at the same price) or at 33 lower cost (at equivalent accuracy) than traditional single-expert grading. Mechanical Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities. Detecting and correcting low quality submissions in crowdsourcing tasks is an important challenge. Prior work has primarily focused on worker outcomes or reputation, using approaches such as agreement across workers or with a gold standard to evaluate quality. We propose an alternative and complementary technique that focuses on the way workers work rather than the products they produce. Our technique captures behavioral traces from online crowd workers and uses them to predict outcome measures such quality, errors, and the likelihood of cheating. We evaluate the effectiveness of the approach across three contexts including classification, generation, and comprehension tasks. The results indicate that we can build predictive models of task performance based on behavioral traces alone, and that these models generalize to related tasks. Finally, we discuss limitations and extensions of the approach.