Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions

Autores
Monasterio, Juan de
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Fraud in the mobile advertising world is a topic gaining momentum recently. Different reports agree that invalid traffc is generating losses in the order of billions of dollars and that there is a signi cant amount of fraud with ongoing efforts against it. Here at Jampp1 we use an automated fraud detection algorithm for a recent type of mobile advertising fraud, which can be referred to as click-spamming, click-injection or mobile-hijacking. We propose a metric to measure suspicious installs, and use a heuristic to compare the ts of theoretical distributions to this metric. This allows us to derive a threshold for suspicious installs. Our metric is based on the time-delta distributions, which amounts to the time it takes from a click in an ad to be converted into an install. The model is currently in use with satisfactory results. To the best of our knowledge, this is the rst algorithm in production used to tackle this speci c kind of fraud.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
Fraude
publicidad
Teléfono Celular
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/63175

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network_name_str SEDICI (UNLP)
spelling Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta DistributionsMonasterio, Juan deCiencias InformáticasFraudepublicidadTeléfono CelularFraud in the mobile advertising world is a topic gaining momentum recently. Different reports agree that invalid traffc is generating losses in the order of billions of dollars and that there is a signi cant amount of fraud with ongoing efforts against it. Here at Jampp1 we use an automated fraud detection algorithm for a recent type of mobile advertising fraud, which can be referred to as click-spamming, click-injection or mobile-hijacking. We propose a metric to measure suspicious installs, and use a heuristic to compare the ts of theoretical distributions to this metric. This allows us to derive a threshold for suspicious installs. Our metric is based on the time-delta distributions, which amounts to the time it takes from a click in an ad to be converted into an install. The model is currently in use with satisfactory results. To the best of our knowledge, this is the rst algorithm in production used to tackle this speci c kind of fraud.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf23-32http://sedici.unlp.edu.ar/handle/10915/63175enginfo:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/AGRANDA/AGRANDA-07.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7569info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:40:52Zoai:sedici.unlp.edu.ar:10915/63175Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:40:53.051SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
title Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
spellingShingle Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
Monasterio, Juan de
Ciencias Informáticas
Fraude
publicidad
Teléfono Celular
title_short Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
title_full Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
title_fullStr Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
title_full_unstemmed Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
title_sort Tagging Click-Spamming Suspicious Installs in Mobile Advertising Through Time Delta Distributions
dc.creator.none.fl_str_mv Monasterio, Juan de
author Monasterio, Juan de
author_facet Monasterio, Juan de
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Fraude
publicidad
Teléfono Celular
topic Ciencias Informáticas
Fraude
publicidad
Teléfono Celular
dc.description.none.fl_txt_mv Fraud in the mobile advertising world is a topic gaining momentum recently. Different reports agree that invalid traffc is generating losses in the order of billions of dollars and that there is a signi cant amount of fraud with ongoing efforts against it. Here at Jampp1 we use an automated fraud detection algorithm for a recent type of mobile advertising fraud, which can be referred to as click-spamming, click-injection or mobile-hijacking. We propose a metric to measure suspicious installs, and use a heuristic to compare the ts of theoretical distributions to this metric. This allows us to derive a threshold for suspicious installs. Our metric is based on the time-delta distributions, which amounts to the time it takes from a click in an ad to be converted into an install. The model is currently in use with satisfactory results. To the best of our knowledge, this is the rst algorithm in production used to tackle this speci c kind of fraud.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Fraud in the mobile advertising world is a topic gaining momentum recently. Different reports agree that invalid traffc is generating losses in the order of billions of dollars and that there is a signi cant amount of fraud with ongoing efforts against it. Here at Jampp1 we use an automated fraud detection algorithm for a recent type of mobile advertising fraud, which can be referred to as click-spamming, click-injection or mobile-hijacking. We propose a metric to measure suspicious installs, and use a heuristic to compare the ts of theoretical distributions to this metric. This allows us to derive a threshold for suspicious installs. Our metric is based on the time-delta distributions, which amounts to the time it takes from a click in an ad to be converted into an install. The model is currently in use with satisfactory results. To the best of our knowledge, this is the rst algorithm in production used to tackle this speci c kind of fraud.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
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info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/63175
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dc.language.none.fl_str_mv eng
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info:eu-repo/semantics/altIdentifier/issn/2451-7569
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-sa/3.0/
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rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
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