Click Stream Data Analysis for Online Fraud Detection in E-Commerce

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Click Stream Data Analysis for Online Fraud Detection in E-Commerce

Popis výsledku v původním jazyce

Web services became the integration part of our life at the present time including advertisement on various web pages. Many e-commerce companies generate advertisement revenue by selling clicks (it is known as Pay-Per-Click model). In this model, e-commerce company is paid for each time an advertisement link on its website is clicked leading to the sponsoring company's content. However, some of these companies inflate the number of clicks their sites generate. Generation of such invalid clicks either by humans or software with the intension to get fraudulently money is known as click fraud. In this article we show how the click fraud can be unmasked using various time features (e.g., period of the day and the day of the week when a user's (that is identified by his IP address) clicking occur). We combine several different time features into a timeprint. We use machine learning methods in a number of experiments to get an understanding of to what extent time prints can be used for identifying click fraud. The obtained results show that timeprints indeed can be a useful tool for the improvement of the quality of click fraud analysis.

Název v anglickém jazyce

Click Stream Data Analysis for Online Fraud Detection in E-Commerce

Popis výsledku anglicky

Web services became the integration part of our life at the present time including advertisement on various web pages. Many e-commerce companies generate advertisement revenue by selling clicks (it is known as Pay-Per-Click model). In this model, e-commerce company is paid for each time an advertisement link on its website is clicked leading to the sponsoring company's content. However, some of these companies inflate the number of clicks their sites generate. Generation of such invalid clicks either by humans or software with the intension to get fraudulently money is known as click fraud. In this article we show how the click fraud can be unmasked using various time features (e.g., period of the day and the day of the week when a user's (that is identified by his IP address) clicking occur). We combine several different time features into a timeprint. We use machine learning methods in a number of experiments to get an understanding of to what extent time prints can be used for identifying click fraud. The obtained results show that timeprints indeed can be a useful tool for the improvement of the quality of click fraud analysis.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 10th International Scientific Conference INPROFORUM. Threatened Europe - Socio-Economic and Environmental Changes

ISBN

978-80-7394-607-4

ISSN

2336-6788

e-ISSN

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Počet stran výsledku

6

Strana od-do

175-180

Název nakladatele

Jihočeská univerzita v Českých Budějovicích, Ekonomická fakulta

Místo vydání

České Budějovice

Místo konání akce

České Budějovice

Datum konání akce

3. 11. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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