On Hit Inflation Techniques and Detection in Streams of Web Advertising Networks

Metwally, Ahmed; Agrawal, Divyakant; Abbadi, Amr El; Zheng, Qi

doi:10.1109/icdcs.2007.124

Cited by 37 publications

(26 citation statements)

References 16 publications

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“…Click fraud on a larger scale uses a script to simulate human clicking or make visitors automatically click on ads [143][144][145][146][147][148][149][150][151][152]. Botnets can also be used for click fraud [153].…”

Section: How It Workmentioning

confidence: 99%

“…It cannot be detected by the above method, since the clicks are from many different visitors. It is necessary to monitor the click-through rate (CTR), which is the number of clicks divided by that of impressions (i.e., ad views) [147,150]. It is an estimate of the probability in the ad network that an impression leads to a click.…”

Section: Forms Of Click Fraud and Technical Responsesmentioning

confidence: 99%

See 1 more Smart Citation

The dark side of the Internet: Attacks, costs and responses

Kim

Jeong

Kim

et al. 2011

Information Systems

159

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Section: How It Workmentioning

confidence: 99%

Section: Forms Of Click Fraud and Technical Responsesmentioning

confidence: 99%

The dark side of the Internet: Attacks, costs and responses

Kim

Jeong

Kim

et al. 2011

Information Systems

159

View full text Add to dashboard Cite

“…Click fraud schemes [23] have the dual effect of inflating the number of "click-throughs" on the Ads posted on a website and increasing the popularity of the website as the number of hits increases. While click-fraud may directly affect the ranks produced by direct counting schemes (e.g., those of Alexa), its effect on our ranks is more limited.…”

Section: Resilience To Fraudulent Inflationmentioning

confidence: 99%

“…However, since the type of sanitization that is applied, as well as the specific data collected, are completely at the discretion of the data collector, large cross-sections of the population shy away from deploying these agents. Moreover, the mere clientside nature of these collection schemes opens the door to abuse (be it via click fraud [23] or other manipulation recipes [1]) that directly affect the integrity of the results. Recently, the prevalence of these fraudulent behaviors has raised so much doubts about the integrity and authenticity of these ranking measures that it captured the attention of the mainstream press (e.g., [22]).…”

Section: Introductionmentioning

confidence: 99%

Peeking Through the Cloud: DNS-Based Estimation and Its Applications

Rajab

Monrose

Terzis

et al. 2008

Applied Cryptography and Network Security

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Abstract. Reliable network demographics are quickly becoming a much sought-after digital commodity. However, as the need for more refined Internet demographics has grown, so too has the tension between privacy and utility. Unfortunately, current techniques lean too much in favor of functional requirements over protecting the privacy of users. For example, the most prominent proposals for measuring the relative popularity of a website depend on the deployment of client-side measurement agents that are generally perceived as infringing on users' privacy, thereby limiting their wide scale adoption. Moreover, the client-side nature of these techniques also makes them susceptible to various manipulation tactics that undermine the integrity of their results. In this paper, we propose a new estimation technique that uses DNS cache probing to infer the density of clients accessing a given service. Compared to earlier techniques, our scheme is less invasive as it does not reveal user-specific traits, and is more robust against manipulation. We demonstrate the flexibility of our approach through two important security applications. First, we illustrate how our scheme can be used as a lightweight technique for measuring and verifying the relative popularity rank of different websites. Second, using data from several hundred botnets, we apply our technique to indirectly measure the infected population of this increasing Internet phenomenon.

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“…Multi stream analysis arises in many contexts, including large scale web advertising networks, the internet, sensor networks, and real time data analysis [6]. In large scale web-based advertising networks, clicks originating from different internet hosts result in multiple click streams that need to be merged at a single server for analysis and identification of suspicious publishers or IP addresses [21]. In the Internet, network monitors at core routers process streams of packets arriving from different origins for accounting purposes, and detection of malicious activities such as Distributed Denial of Service attacks [29].…”

Section: Introductionmentioning

confidence: 99%

Thread cooperation in multicore architectures for frequency counting over multiple data streams

et al. 2009

Self Cite

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Many real-world data stream analysis applications such as network monitoring, click stream analysis, and others require combining multiple streams of data arriving from multiple sources. This is referred to as multi-stream analysis. To deal with high stream arrival rates, it is desirable that such systems be capable of supporting very high processing throughput. The advent of multicore processors and powerful servers driven by these processors calls for efficient parallel designs that can effectively utilize the parallelism of the multicores, since performance improvement is possible only through effective parallelism. In this paper, we address the problem of parallelizing multi-stream analysis in the context of multicore processors. Specifically, we concentrate on parallelizing frequent elements, top-k, and frequency counting over multiple streams. We discuss the challenges in designing an efficient parallel system for multi-stream processing. Our evaluation and analysis reveals that traditional "contention" based locking results in excessive overhead and wait, which in turn leads to severe performance degradation in modern multicore architectures. Based on our analysis, we propose a "cooperation" based locking paradigm for efficient parallelization of frequency counting. The proposed "cooperation" based paradigm removes waits associated with synchronization, and allows replacing locks by much cheaper atomic synchronization primitives. Our implementation of the proposed paradigm to parallelize a well known frequency counting algorithm shows the benefits of the proposed "cooperation" based locking paradigm when compared to the traditional "contention" based locking paradigm. In our experiments, the proposed "cooperation" based design outperforms the traditional "contention" based design by a factor of 2 − 5.5X for synthetic zipfian data sets.

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On Hit Inflation Techniques and Detection in Streams of Web Advertising Networks

Cited by 37 publications

References 16 publications

The dark side of the Internet: Attacks, costs and responses

The dark side of the Internet: Attacks, costs and responses

Peeking Through the Cloud: DNS-Based Estimation and Its Applications

Thread cooperation in multicore architectures for frequency counting over multiple data streams

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