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.
Threshold-based approaches detect hotspots of activity between click-malware and publishers. One set of techniques detect traffic hotspots @cite_57 @cite_4 @cite_44 . Another technique is to examine publisher-user pairs with above-average click rates @cite_1 . All the techniques in this approach develop a normative baseline of activity and detect malicious behaviour beyond a threshold distance from the baseline. The idea is that fraudsters need to scale their activity to a level where their turnover (from a click fraud campaign) covers their costs as well as generate a profit.
@cite_57 @cite_44 @cite_1 @cite_4
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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. Several data management challenges arise in the context of Internet advertising networks, where Internet advertisers pay Internet publishers to display advertisements on their Web sites and drive traffic to the advertisers from surfers' clicks. Although advertisers can target appropriate market segments, the model allows dishonest publishers to defraud the advertisers by simulating fake traffic to their own sites to claim more revenue. This paper addresses the case of publishers launching fraud attacks from numerous machines, which is the most widespread scenario. The difficulty of uncovering these attacks is proportional to the number of machines and resources exploited by the fraudsters. In general, detecting this class of fraud entails solving a new data mining problem, which is finding correlations in multidimensional data. Since the dimensions have large cardinalities, the search space is huge, which has long allowed dishonest publishers to inflate their traffic, and deplete the advertisers' advertising budgets. We devise the approximate SLEUTH algorithms to solve the problem efficiently, and uncover single-publisher frauds. We demonstrate the effectiveness of SLEUTH both analytically and by reporting some of its results on the Fastclick network, where numerous fraudsters were discovered. Click-spam in online advertising, where unethical publishers use malware or trick users into clicking ads, siphons off hundreds of millions of advertiser dollars meant to support free websites and apps. Ad networks today, sadly, rely primarily on security through obscurity to defend against click-spam. In this paper, we present Viceroi, a principled approach to catching click-spam in search ad networks. It is designed based on the intuition that click-spam is a profit-making business that needs to deliver higher return on investment (ROI) for click-spammers than other (ethical) business models to offset the risk of getting caught. Viceroi operates at the ad network where it has visibility into all ad clicks. Working with a large real-world ad network, we find that the simple-yet-general Viceroi approach catches over six very different classes of click-spam attacks (e.g., malware-driven, search-hijacking, arbitrage) without any tuning knobs. 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.