Click fraud : how to spot it, how to stop it?

Online search advertising is currently the greatest source of revenue for many Internet giants such as Google™, Yahoo!™, and Bing™. The increased number of 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 even 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. Most academics and consultants who study online advertising estimate that 15% to 35% of ads in pay per click (PPC) online advertising systems are not authentic. In the first two quarters of 2010, US marketers alone spent $5.7 billion on PPC ads, where PPC ads are between 45 and 50 percent of all online ad spending. On average about$1.5 billion is wasted due to click-fraud. These fraudulent clicks are believed to be initiated by users in poor countries, or botnets, who are trained to click on specific ads. For example, according to a 2010 study from Information Warfare Monitor, the operators of Koobface, a program that installed malicious software to participate in click fraud, made over $2 million in just over a year. The process of making such illegitimate clicks to generate revenue is called click-fraud. Search engines claim they filter out most questionable clicks and either not charge for them or reimburse advertisers that have been wrongly billed. However this is a hard task, despite the claims that brokers\u27 efforts are satisfactory. In the simplest scenario, a publisher continuously clicks on the ads displayed on his own website in order to make revenue. In a more complicated scenario. a travel agent may hire a large, globally distributed, botnet to click on its competitor\u27s ads, hence depleting their daily budget. We analyzed those different types of click fraud methods and proposed new methodologies to detect and prevent them real time. While traditional commercial approaches detect only some specific types of click fraud, Collaborative Click Fraud Detection and Prevention (CCFDP) system, an architecture that we have implemented based on the proposed methodologies, can detect and prevents all major types of click fraud. The proposed solution analyzes the detailed user activities on both, the server side and client side collaboratively to better describe the intention of the click. Data fusion techniques are developed to combine evidences from several data mining models and to obtain a better estimation of the quality of the click traffic. Our ideas are experimented through the development of the Collaborative Click Fraud Detection and Prevention (CCFDP) system. Experimental results show that the CCFDP system is better than the existing commercial click fraud solution in three major aspects: 1) detecting more click fraud especially clicks generated by software; 2) providing prevention ability; 3) proposing the concept of click quality score for click quality estimation. In the CCFDP initial version, we analyzed the performances of the click fraud detection and prediction model by using a rule base algorithm, which is similar to most of the existing systems. We have assigned a quality score for each click instead of classifying the click as fraud or genuine, because it is hard to get solid evidence of click fraud just based on the data collected, and it is difficult to determine the real intention of users who make the clicks. Results from initial version revealed that the diversity of CF attack Results from initial version revealed that the diversity of CF attack types makes it hard for a single counter measure to prevent click fraud. Therefore, it is important to be able to combine multiple measures capable of effective protection from click fraud. Therefore, in the CCFDP improved version, we provide the traffic quality score as a combination of evidence from several data mining algorithms. We have tested the system with a data from an actual ad campaign in 2007 and 2008. We have compared the results with Google Adwords reports for the same campaign. Results show that a higher percentage of click fraud present even with the most popular search engine. The multiple model based CCFDP always estimated less valid traffic compare to Google. Sometimes the difference is as high as 53%. Detection of duplicates, fast and efficient, is one of the most important requirement in any click fraud solution. Usually duplicate detection algorithms run in real time. In order to provide real time results, solution providers should utilize data structures that can be updated in real time. In addition, space requirement to hold data should be minimum. In this dissertation, we also addressed the problem of detecting duplicate clicks in pay-per-click streams. We proposed a simple data structure, Temporal Stateful Bloom Filter (TSBF), an extension to the regular Bloom Filter and Counting Bloom Filter. The bit vector in the Bloom Filter was replaced with a status vector. Duplicate detection results of TSBF method is compared with Buffering, FPBuffering, and CBF methods. False positive rate of TSBF is less than 1% and it does not have false negatives. Space requirement of TSBF is minimal among other solutions. Even though Buffering does not have either false positives or false negatives its space requirement increases exponentially with the size of the stream data size. When the false positive rate of the FPBuffering is set to 1% its false negative rate jumps to around 5%, which will not be tolerated by most of the streaming data applications. We also compared the TSBF results with CBF. TSBF uses only half the space or less than standard CBF with the same false positive probability. One of the biggest successes with CCFDP is the discovery of new mercantile click bot, the Smart ClickBot. We presented a Bayesian approach for detecting the Smart ClickBot type clicks. The system combines evidence extracted from web server sessions to determine the final class of each click. Some of these evidences can be used alone, while some can be used in combination with other features for the click bot detection. During training and testing we also addressed the class imbalance problem. Our best classifier shows recall of 94%. and precision of 89%, with F1 measure calculated as 92%. The high accuracy of our system proves the effectiveness of the proposed methodology. Since the Smart ClickBot is a sophisticated click bot that manipulate every possible parameters to go undetected, the techniques that we discussed here can lead to detection of other types of software bots too. Despite the enormous capabilities of modern machine learning and data mining techniques in modeling complicated problems, most of the available click fraud detection systems are rule-based. Click fraud solution providers keep the rules as a secret weapon and bargain with others to prove their superiority. We proposed validation framework to acquire another model of the clicks data that is not rule dependent, a model that learns the inherent statistical regularities of the data. Then the output of both models is compared. Due to the uniqueness of the CCFDP system architecture, it is better than current commercial solution and search engine/ISP solution. The system protects Pay-Per-Click advertisers from click fraud and improves their Return on Investment (ROI). The system can also provide an arbitration system for advertiser and PPC publisher whenever the click fraud argument arises. Advertisers can gain their confidence on PPC advertisement by having a channel to argue the traffic quality with big search engine publishers. The results of this system will booster the internet economy by eliminating the shortcoming of PPC business model. General consumer will gain their confidence on internet business model by reducing fraudulent activities which are numerous in current virtual internet world

Context-related acoustic variation in male fallow deer (Dama dama) groans

While social and behavioural contexts are known to affect the acoustic structure of vocal signals in several mammal species, few studies have investigated context-related acoustic variation during inter-sexual advertisement and/or intra-sexual competition. Here we recorded male fallow deer groans during the breeding season and investigated how key acoustic parameters (fundamental frequency and formant frequencies) vary as a function of the social context in which they are produced. We found that in the presence of females, male fallow deer produced groans with higher mean fundamental frequency when vocal males were also present than they did when no vocal males were in close vicinity. We attribute this to the increased arousal state typically associated with this context. In addition, groan minimum formant frequency spacing was slightly, but significantly lower (indicating marginally more extended vocal tracts) when males were alone than when potential mates and/or competitors were nearby. This indicates that, contrary to our predictions, male fallow deer do not exaggerate the acoustic impression of their body size by further lowering their formant frequencies in the presence of potential mating partners and competitors. Furthermore, since the magnitude of the variation in groan minimum formant frequency spacing remains small compared to documented inter-individual differences, our findings are consistent with the hypothesis that formants are reliable static cues to body size during intra- and inter-sexual advertisement that do not concurrently encode dynamic motivation-related informatio

Consumer reactions to self-expressive brand display

Brand names and other brand elements are often displayed on one’s body or clothes for the purpose of personal value expression. Despite the frequency of such brand displays in the marketplace, we know little about how consumers respond to seeing brands in this fashion. A recent view of consumer brand identification—the concept of brand engagement in self-concept (BESC)—provides a unique perspective from which to explore how consumers react when see-ing brands displayed by others. Across three experiments, we demonstrate a consistent pattern of findings indicating that consumers’ reactions to others ostentatiously displaying brands as means of value expression are strongest for those with high BESC levels and with a high value focus during brand exposure. The research highlights important variations in consumers’ responses to self-expressive brand stimuli associated with others; implications for branding practice and re-search are provided.Brand engagement; self-concept; advertising; brand management

How to portray men and women in advertisements? Explicit and implicit evaluations of ads depicting different gender roles.

The purpose of the current study was to gain more insight in the evaluation of advertisements containing different gender role portrayals (stereotypical/a-stereotypical) by examining explicit and implicit processes of ad evaluation. The results of two experiments showed an explicit preference for ads containing a-stereotypical images. Implicitly, we found a preference for 'warm' ads irrespective of the degree of gender stereotypicality of the ad. These findings suggest that complex stimuli such as ads may inhibit implicit gender stereotype activation. At an implicit level, warmth seems a better predictor of ad evaluation.Ad evaluation; Evaluation; Gender role portrayal; Image; Implicit association test; Implicit attitudes; Implicit stereotyping; Preference; Processes; Research; Roles; Studies;

Selling Favors in the Lab: Experiments on Campaign Finance Reform

Substantial academic interest and public policy debate centers on campaign finance reform. Campaign resources can provide benefits to constituencies if candidates use them to fund the distribution of useful information. On the other hand, voters can potentially be harmed if candidates trade policy favors to special interests in exchange for contributions. Unfortunately, because informative field data on this topic are very difficult to obtain, the effects of different campaign finance strategies on election outcomes and economic welfare remain largely uninformed by empirical analyses. This paper reports data from novel laboratory experiments designed to shed light on the campaign finance debate. Our experiment is based on a model where power-hungry candidates are motivated to trade favors for campaign contributions. Our data is consistent with the model’s predictions. We find that voters’ revise their beliefs in response to candidate advertising in a way that is consistent with theory. Moreover, in relation to privately financed electoral competitions, in publicly financed campaigns (i) high-quality candidates are elected more frequently, and (ii) margins of victory are larger. Both of these outcomes are predicted by theory. We conduct policy experiments on various campaign finance strategies, including the widely suggested caps on private fundraising. Our results suggest that caps can improve voter welfare but do not increase the likelihood that high-quality candidates will be elected.