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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/63175
Ver los metadatos del registro completo
id |
SEDICI_00cd394f827282b1b0b39a77b0224bfa |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/63175 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
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 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/63175 |
url |
http://sedici.unlp.edu.ar/handle/10915/63175 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/AGRANDA/AGRANDA-07.pdf 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/ Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-sa/3.0/ Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) |
dc.format.none.fl_str_mv |
application/pdf 23-32 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1842260274617253888 |
score |
13.13397 |