Crowdsourcing for click fraud detection

Mouawi, Riwa; Elhajj, Imad H.; Chehab, Ali; Kayssi, Ayman

doi:10.1186/s13635-019-0095-1

Cited by 19 publications

(4 citation statements)

References 38 publications

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“…App developers can generate fraudulent ad clicks in an automated manner, resulting in losses for advertisers. Recent studies have examined the vulnerability of ad networks to click fraud attacks and proposed techniques for ad networks to detect click fraud (Cho et al, 2016;Mouawi et al, 2019). Cho et al (2016) note that there is a dearth of research on the vulnerability of ad networks to different varieties of ad click threats and methods to prevent them.…”

Section: Theme 4: Ad Fraudmentioning

confidence: 99%

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In-app advertising: a systematic literature review and implications for future research

Maddodi,

Upadhyaya

2023

SJME

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Purpose The purpose of this study is to review and synthesize the literature on in-app advertising, identify gaps and propose future research directions. Design/methodology/approach The authors use a systematic literature review (SLR) approach, following the PRISMA guidelines, to investigate the current state of research in in-app advertising. The study uses 44 shortlisted articles from the Scopus and Web of Science databases. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the authors analyze the gaps in theory, context, characteristics and methods. Findings Using thematic analysis, the authors identify five main themes in the in-app advertising literature, namely, ad platform optimization; mobile app user psychology and behavior; ad effectiveness; ad fraud; and security, privacy and other user concerns. The findings show the need for empirical research, with a strong theoretical foundation in emerging ad formats of in-app advertising, user behavior and buy-side of in-app advertising. Originality/value This is a maiden study to conduct a domain-based SLR in the emerging field of in-app advertising using the TCCM framework. The authors highlight the key differences between in-app advertising and mobile web advertising. The authors propose theories in the advertising field that could be used in future empirical studies of in-app advertising.

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Section: Theme 4: Ad Fraudmentioning

confidence: 99%

Section: Thematic Analysis Of In-app Advertising Literaturementioning

confidence: 99%

In-app advertising: a systematic literature review and implications for future research

Maddodi,

Upadhyaya

2023

SJME

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“…Detection of fraud has become a serious challenge in today's complex business scenario (Abdallah et al, 2016;Mouawi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Mediating Role of Big Data to Influence Practitioners to Use Forensic Accounting for Fraud Detection

Mittal¹,

Kaur²,

Gupta³

2021

EJOBSAT

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Globally, the financial industry in the recent times has witnessed various forms of fraudulent activities in the financial markets creating dilemma for the professionals, and the auditors who owns responsibility of ensuring accuracy and transparency. This article aims at finding the emergence of Big Data technology to fraud and forensic accounting by practitioner accountants in in India. A research model and hypotheses has been developed to examine the relationship between the awareness level of forensic accounting, Big Data and intentions to use it for fraud detection using structural equation modeling. Results indicate that awareness of forensic accounting has a positive influence on practitioners' intentions to its use for fraud detection. Big data technologies mediate the relationship between awareness and intentions to use for fraud detection. The results of the study are useful in implementation of Big Data technologies into the forensic accounting domain that can facilitate combating fraud.

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“…However, despite the obvious need for privacy, unambiguous identification of the device or the browser is extremely helpful in ensuring security and preventing abusive practices on the Internet. Numerous fraudulent activities include credit card payment scams [3], artificial generation of Internet traffic, the so-called abusive traffic [4], generating click fraud [5,6] or automating the collection of web site contents [7]. Based on various reports [8,9], losses due to fraud in online advertising alone can range from 6.5 to 19 billion dollars.…”

Section: Introductionmentioning

confidence: 99%

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

Gabryel

Grzanek

Hayashi

2020

Journal of Artificial Intelligence and Soft Computing Research

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Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.

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Crowdsourcing for click fraud detection

Cited by 19 publications

References 38 publications

In-app advertising: a systematic literature review and implications for future research

In-app advertising: a systematic literature review and implications for future research

The Mediating Role of Big Data to Influence Practitioners to Use Forensic Accounting for Fraud Detection

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

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