ViceROI

Dave, Vacha; Guha, Saikat; Zhang, Yin

doi:10.1145/2508859.2516688

Cited by 42 publications

(11 citation statements)

References 14 publications

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“…The focus of this work is on generic click fraud defences, rather than analysing individual click-modules. Current efforts to defeat click fraud have primarily focused on fraud measurement techniques [11], measurement and analysis of publisher fraud [7,12], and ad placement fraud [29]. While publisher and affiliate-marketing fraud are doubtless of importance, there is limited work that focuses on detecting click fraud from ad network clickstreams supplied to advertisers.…”

Section: Figure 1: Organic and Non-organic Click Fraudmentioning

confidence: 99%

“…Upon evaluation of our defence, we report significantly improved detection and estimation over past efforts [11,12,[31][32][33]. Contributions: First, we have developed two clickspam defenses: the mimicry defence and the bait-click defence.…”

Section: Figure 1: Organic and Non-organic Click Fraudmentioning

confidence: 99%

“…One set of techniques detect traffic hotspots [31][32][33]. Another technique is to examine publisher-user pairs with above-average click rates [12]. All the techniques in this approach develop a normative baseline of activity and detect malicious behaviour beyond a threshold distance from the baseline.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…Early threshold-based defences demonstrated a focus on the volumes of click fraud from bad-listed sources, but failed simply because attackers were able to swiftly discard their publisher accounts after receiving reputational hits from ad network defences [12,51], and opened new ones. The decrease in the cost of botnet rentals in the underground economy has been a primary driver of fraud [1, 38,47].…”

Section: Introductionmentioning

confidence: 99%

“…Assuming the earning potential of 0.5 cents per click, which is at the lower end of the spectrum, an attacker can cover operational costs with just three to five fake clicks a day, with a few more to run their operation profitably. Based on this, traditional threshold-based defences [11,12,32,33] fail as the levels of click fraud per source goes below the detection threshold.…”

Section: Introductionmentioning

confidence: 99%

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Clicktok

Nagaraja

Shah

2019

Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks

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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.

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Section: Figure 1: Organic and Non-organic Click Fraudmentioning

confidence: 99%

Section: Figure 1: Organic and Non-organic Click Fraudmentioning

confidence: 99%

Section: Comparison With Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clicktok

Nagaraja

Shah

2019

Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks

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Protecting Internet users from becoming victimized attackers of click‐fraud

Iqbal

Zulkernine

Jaafar³

et al. 2017

J Software Evolu Process

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Internet users are often victimized by malicious attackers. Some attackers infect and use innocent users' machines to launch large‐scale attacks without the users' knowledge. One of such attacks is the click‐fraud attack. Click‐fraud happens in pay‐per‐click ad networks where the ad network charges advertisers for every click on their ads. Click‐fraud has been proved to be a serious problem for the online advertisement industry. In a click‐fraud attack, a user or an automated software clicks on an ad with a malicious intent and advertisers need to pay for those valueless clicks. Among many forms of click‐fraud, botnets with the automated clickers are the most severe ones. In this study, we present a method for detecting automated clickers from the user side. The proposed method to fight click‐fraud, FCFraud, can be integrated into the desktop and smart device operating systems. Since most modern operating systems already provide some kind of antimalware service, our proposed method can be implemented as a part of the service. We believe that an effective protection at the operating system level can save billions of dollars of the advertisers. Experiments show that FCFraud is 99.6% (98.2% in mobile ad library–generated traffic) accurate in classifying ad requests from all user processes and it is 100% successful in detecting clickbots in both desktop and mobile devices. We implement a cloud backend for the FCFraud service to save battery power in mobile devices. The overhead of executing FCFraud is also analyzed and we show that it is reasonable for both the platforms.

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