Advertising is a primary means for revenue generation for millions of websites and smartphone apps (publishers). Naturally, a fraction of publishers abuse the ad-network to systematically defraud advertisers of their money. Defenses have matured to overcome some forms of click fraud but are inadequate against the threat of organic click fraud attacks. Malware detection systems including honeypots fail to stop click fraud apps; ad-network filters are better but measurement studies have reported that a third of the clicks supplied by ad-networks are fake; collaborations between ad-networks and app stores that bad-lists malicious apps works better still, but fails to prevent criminals from writing fraudulent apps which they monetise until they get banned and start over again. This work develops novel inference techniques that can isolate click fraud attacks using their fundamental properties. In the mimicry defence , we leverage the observation that organic click fraud involves the re-use of legitimate clicks. Thus we can isolate fake-clicks by detecting patterns of click-reuse within ad-network clickstreams with historical behaviour serving as a baseline. Second, in bait-click defence . we leverage the vantage point of an ad-network to inject a pattern of bait clicks into the user's device, to trigger click fraud-apps that are gated on user-behaviour. Our experiments show that the mimicry defence detects around 81 of fake-clicks in stealthy (low rate) attacks with a false-positive rate of 110110 per hundred thousand clicks. Bait-click defence enables further improvements in detection rates of 95 and reduction in false-positive rates of between 0 and 30 clicks per million, a substantial improvement over current approaches.
Several papers have highlighted the importance of the field of advertising and click fraud @cite_6 @cite_2 . Keeping ad networks free of fraud is highlighted by the work of @cite_33 , who show that ad networks free of click fraud results in a competitive advantage over rival ad networks and thus attracts more advertisers. Research shows that even the largest advertising platforms are affected by click fraud @cite_29 and are tackling the problem, by primarily employing data mining techniques to distinguish legitimate, fraudulent or bot-generated click events. Clickbots are a leading attack vector for carrying out click fraud; around 30 major ad networks @cite_35 @cite_58 and originate from malware networks (rent-a-botnet services).
@cite_35 @cite_33 @cite_29 @cite_6 @cite_2 @cite_58
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Click fraud-the deliberate clicking on advertisements with no real interest on the product or service offered-is one of the most daunting problems in online advertising. Building an effective fraud detection method is thus pivotal for online advertising businesses. We organized a Fraud Detection in Mobile Advertising (FDMA) 2012 Competition, opening the opportunity for participants to work on real-world fraud data from BuzzCity Pte. Ltd., a global mobile advertising company based in Singapore. In particular, the task is to identify fraudulent publishers who generate illegitimate clicks, and distinguish them from normal publishers. The competition was held from September 1 to September 30, 2012, attracting 127 teams from more than 15 countries. The mobile advertising data are unique and complex, involving heterogeneous information, noisy patterns with missing values, and highly imbalanced class distribution. The competition results provide a comprehensive study on the usability of data mining-based fraud detection approaches in practical setting. Our principal findings are that features derived from fine-grained time-series analysis are crucial for accurate fraud detection, and that ensemble methods offer promising solutions to highly-imbalanced nonlinear classification tasks with mixed variable types and noisy missing patterns. The competition data remain available for further studies at http: palanteer.sis.smu.edu.sg fdma2012 . Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of the content publishers' sites. Some publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors ' advertising budgets. In this paper, we describe the advertising network model, and discuss the issue of fraud that is an integral problem in such setting. We propose using online algorithms on aggregate data to accurately and proactively detect automated traffic, preserve surfers' privacy, while not altering the industry model. We provide a complete classification of the hit inflation techniques; and devise stream analysis techniques that detect a variety of fraud attacks. We abstract detecting the fraud attacks of some classes as theoretical stream analysis problems that we bring to the data management research community as open problems. A framework is outlined for deploying the proposed detection algorithms on a generic architecture. We conclude by some successful preliminary findings of our attempt to detect fraud on a real network. Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach. Online advertising is currently the richest source of revenue for many Internet giants. The increased number of online businesses, specialized websites and modern profiling techniques have all contributed to an explosion of the income of ad brokers from online advertising. The single biggest threat to this growth, is however, click-fraud. Trained botnets and individuals are hired by click-fraud specialists in order to maximize the revenue of certain users from the ads they publish on their websites, or to launch an attack between competing businesses. In this note we wish to raise the awareness of the networking research community on potential research areas within the online advertising field. As an example strategy, we present Bluff ads; a class of ads that join forces in order to increase the effort level for click-fraud spammers. Bluff ads are either targeted ads, with irrelevant display text, or highly relevant display text, with irrelevant targeting information. They act as a litmus test for the legitimacy of the individual clicking on the ads. Together with standard threshold-based methods, fake ads help to decrease click-fraud levels.