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Title:
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Automatic detection of click fraud in online advertisements |
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Description:
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Increasing advancement , access and availability of the Internet Technology have intensified the growth of the Internet users over the last decade . This has made online advertising a popular venue for many companies to market their products and services . Today , online advertisement is one of the most important sources of revenues that impact the economy of many large enterprises . In online advertisement , an advertiser pays a broker (e .g . , Google , Yahoo ) , who normally has a search engine , to post its online advertisement , which can be on any appropriate publisher site . The publisher earns revenues from the broker for each click on the advertisement posted on its site , while the advertiser will be charged . Thus , when an excessive number of clicks occur , this can quickly dry up the fund of a rival company and drive it out of the competing advertisement . At the same time , each click adds revenue to the publisher . This motivates click frauds , which refer to malicious acts to create fraudulent clicks with the intent to increase the revenue or drive away competitors without real interest in the products or services being advertised . Identifying click frauds is a difficult problem because of the dynamic nature of the click behaviors , some of which are generated by humans and some are by automated software called bots . There have been previous work attempting to identify click frauds using various techniques but they tend to be limited by the types of the data , the way they are processing or assumptions that are not always achievable .
This thesis presents an approach to automatically detecting click frauds in online advertising . The approach uses a mathematical theory of evidence to estimate the likelihood of a click whether it is a fraud or genuine using web log data of user’s activities on the advertiser’s website . One advantage of the proposed approach is the fact that the likelihood can be computed for each incoming click and thus , it gives an online computation of the belief that fits well with the dynamic behaviors of users . The thesis describes the approach and evaluates its validity using two real -world case studies . We believe the approach is general in that it can be applied to any scenario . |
Citation
Automatic detection of click fraud in online advertisements.
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