Fighting Online Click-Fraud Using Bluff Ads

Online advertising is currently the greatest source of revenue for many Internet giants. 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. In this note we wish to raise the awareness of the networking research community on potential research areas within this emerging 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.Comment: Draf

The Study on Supervision Model for Online Advertising Click Fraud

Considering the click fraud in the online advertising market, a basic game theoretic model for click fraud is built firstly. In this model, the Ads Network can choose to make click fraud supervision or trust, and advertising publishers can choose to publish advertisement honestly or to cheat. In this paper, we get the result of the mixed strategy Nash equilibrium solution firstly and then we extend the model to the 2-supervision game model, and then discuss the effect factors when the Ads network is punished due to click fraud. Further more, the model considers the influence on click fraud caused by the competitions between the multi-publishers and then get the new result of the Nash equilibrium solution. Based on the analysis above, click fraud can be effectively prevented in the following ways: intensifying the supervision and control process, implementing penalty on advertising network, reducing information asymmetry, choosing the honest publisher to publish advertisement, building the competitive mechanism, evaluating the online advertising effectiveness in time, and signing detailed operational contract in advance. Key words: Online advertising; Click fraud; Supervision model; Nash equilibriu

Behavioural verification: preventing report fraud in decentralized advert distribution systems

Service commissions, which are claimed by Ad-Networks and Publishers, are susceptible to forgery as non-human operators are able to artificially create fictitious traffic on digital platforms for the purpose of committing financial fraud. This places a significant strain on Advertisers who have no effective means of differentiating fabricated Ad-Reports from those which correspond to real consumer activity. To address this problem, we contribute an advert reporting system which utilizes opportunistic networking and a blockchain-inspired construction in order to identify authentic Ad-Reports by determining whether they were composed by honest or dishonest users. What constitutes a user's honesty for our system is the manner in which they access adverts on their mobile device. Dishonest users submit multiple reports over a short period of time while honest users behave as consumers who view adverts at a balanced pace while engaging in typical social activities such as purchasing goods online, moving through space and interacting with other users. We argue that it is hard for dishonest users to fake honest behaviour and we exploit the behavioural patterns of users in order to classify Ad-Reports as real or fabricated. By determining the honesty of the user who submitted a particular report, our system offers a more secure reward-claiming model which protects against fraud while still preserving the user's anonymity

Detecting Spam Publishers By Serving Honeypot Ads

Click fraud, wherein bots or other unauthorized users click on ads to falsely inflate click-through rates, is a major problem in the online ad industry. This disclosure describes a type of ad, known as a honeypot ad, that is not particularly attractive to humans, but for bots is indistinguishable from a genuine ad. Publishers who employ bots to fraudulently inflate click-through rates, or to misrepresent the popularity of their app or website, are detected when the number of clicks on such honeypot ads are substantially larger than the number of clicks on genuine ads

Clicktok : click fraud detection using traffic analysis

Advertising is a primary means for revenue generation for millions of websites and smartphone apps. Naturally, a fraction abuse ad networks to systematically defraud advertisers of their money. Modern defences have matured to overcome some forms of click fraud but measurement studies have reported that a third of clicks supplied by ad networks could be clickspam. Our work develops novel inference techniques which can isolate click fraud attacks using their fundamental properties.We propose two defences, mimicry and bait-click, which provide clickspam detection with substantially improved results over current approaches. Mimicry leverages the observation that organic clickfraud involves the reuse of legitimate click traffic, and thus isolates clickspam by detecting patterns of click reuse within ad network clickstreams. The bait-click defence leverages the vantage point of an ad network to inject a pattern of bait clicks into a user's device. Any organic clickspam generated involving the bait clicks will be subsequently recognisable by the ad network. Our experiments show that the mimicry defence detects around 81% of fake clicks in stealthy (low rate) attacks, with a false-positive rate of 110 per hundred thousand clicks. Similarly, the bait-click defence enables further improvements in detection, with rates of 95% and a reduction in false-positive rates of between 0 and 30 clicks per million - a substantial improvement over current approaches

Challenges of keyword-based location disclosure

A practical solution to location privacy should be incremen-tally deployable. We claim it should hence reconcile the eco-nomic value of location to aggregators, usually ignored by prior works, with a user’s control over her information. Loca-tion information indeed is being collected and used by many mobile services to improve revenues, and this gives rise to a heated debate: Privacy advocates ask for stricter regula-tion on information collection, while companies argue that it would jeopardize the thriving economy of the mobile web. We describe a system that gives users control over their information and does not degrade the data given to aggre-gators. Recognizing that the first challenge is to express lo-cations in a way that is meaningful for advertisers and users, we propose a keyword based design. Keywords characterize locations, let the users inform the system about their sen-sitivity to disclosure, and build information directly usable by an advertiser’s targeting campaign. Our work makes two main contributions: we design a market of location infor-mation based on keywords and we analyze its robustness to attacks using data from ad-networks, geo-located services, and cell networks. Categories and Subject Descriptors Security and Privacy [Human and societal aspects of security and privacy]: Usability in security and privac