Detectives

Detecting coalition hit inflation attacks in advertising networks streams

Ahmed Metwally, Divyakant Agrawal, Amr El Abbadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

70 Citations (Scopus)

Abstract

Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach.

Original languageEnglish
Title of host publication16th International World Wide Web Conference, WWW2007
Pages241-250
Number of pages10
DOIs
Publication statusPublished - 22 Oct 2007
Externally publishedYes
Event16th International World Wide Web Conference, WWW2007 - Banff, AB, Canada
Duration: 8 May 200712 May 2007

Other

Other16th International World Wide Web Conference, WWW2007
CountryCanada
CityBanff, AB
Period8/5/0712/5/07

Fingerprint

Marketing
Internet
Automation
Industry
Experiments

Keywords

  • Approximate set similarity
  • Click spam detection
  • Cliques enumeration
  • Coalition fraud attacks
  • Real data experiments
  • Similarity-sensitive sampling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Metwally, A., Agrawal, D., & El Abbadi, A. (2007). Detectives: Detecting coalition hit inflation attacks in advertising networks streams. In 16th International World Wide Web Conference, WWW2007 (pp. 241-250) https://doi.org/10.1145/1242572.1242606

Detectives : Detecting coalition hit inflation attacks in advertising networks streams. / Metwally, Ahmed; Agrawal, Divyakant; El Abbadi, Amr.

16th International World Wide Web Conference, WWW2007. 2007. p. 241-250.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Metwally, A, Agrawal, D & El Abbadi, A 2007, Detectives: Detecting coalition hit inflation attacks in advertising networks streams. in 16th International World Wide Web Conference, WWW2007. pp. 241-250, 16th International World Wide Web Conference, WWW2007, Banff, AB, Canada, 8/5/07. https://doi.org/10.1145/1242572.1242606
Metwally A, Agrawal D, El Abbadi A. Detectives: Detecting coalition hit inflation attacks in advertising networks streams. In 16th International World Wide Web Conference, WWW2007. 2007. p. 241-250 https://doi.org/10.1145/1242572.1242606
Metwally, Ahmed ; Agrawal, Divyakant ; El Abbadi, Amr. / Detectives : Detecting coalition hit inflation attacks in advertising networks streams. 16th International World Wide Web Conference, WWW2007. 2007. pp. 241-250
@inproceedings{e0e838b048b2441886a262ae5c78d907,
title = "Detectives: Detecting coalition hit inflation attacks in advertising networks streams",
abstract = "Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach.",
keywords = "Approximate set similarity, Click spam detection, Cliques enumeration, Coalition fraud attacks, Real data experiments, Similarity-sensitive sampling",
author = "Ahmed Metwally and Divyakant Agrawal and {El Abbadi}, Amr",
year = "2007",
month = "10",
day = "22",
doi = "10.1145/1242572.1242606",
language = "English",
isbn = "1595936548",
pages = "241--250",
booktitle = "16th International World Wide Web Conference, WWW2007",

}

TY - GEN

T1 - Detectives

T2 - Detecting coalition hit inflation attacks in advertising networks streams

AU - Metwally, Ahmed

AU - Agrawal, Divyakant

AU - El Abbadi, Amr

PY - 2007/10/22

Y1 - 2007/10/22

N2 - Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach.

AB - Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach.

KW - Approximate set similarity

KW - Click spam detection

KW - Cliques enumeration

KW - Coalition fraud attacks

KW - Real data experiments

KW - Similarity-sensitive sampling

UR - http://www.scopus.com/inward/record.url?scp=35348835502&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35348835502&partnerID=8YFLogxK

U2 - 10.1145/1242572.1242606

DO - 10.1145/1242572.1242606

M3 - Conference contribution

SN - 1595936548

SN - 9781595936547

SP - 241

EP - 250

BT - 16th International World Wide Web Conference, WWW2007

ER -