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Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems

Author

Listed:
  • Stylianos S. Mamais

    (School of Computer Science and Informatics, Cardiff University, 5 The Parade, Roath, Cardiff CF24 3AA, UK
    Current address: School of Computer Science and Informatics, Cardiff University, 5 The Parade, Roath, Cardiff CF24 3AA, UK.)

  • George Theodorakopoulos

    (School of Computer Science and Informatics, Cardiff University, 5 The Parade, Roath, Cardiff CF24 3AA, UK
    Current address: School of Computer Science and Informatics, Cardiff University, 5 The Parade, Roath, Cardiff CF24 3AA, UK.)

Abstract

Service commissions, which are claimed by Ad-Networks and Publishers, are susceptible to forgery as non-human operators are able to artificially create fictitious traffic on digital platforms for the purpose of committing financial fraud. This places a significant strain on Advertisers who have no effective means of differentiating fabricated Ad-Reports from those which correspond to real consumer activity. To address this problem, we contribute an advert reporting system which utilizes opportunistic networking and a blockchain-inspired construction in order to identify authentic Ad-Reports by determining whether they were composed by honest or dishonest users. What constitutes a user’s honesty for our system is the manner in which they access adverts on their mobile device. Dishonest users submit multiple reports over a short period of time while honest users behave as consumers who view adverts at a balanced pace while engaging in typical social activities such as purchasing goods online, moving through space and interacting with other users. We argue that it is hard for dishonest users to fake honest behaviour and we exploit the behavioural patterns of users in order to classify Ad-Reports as real or fabricated. By determining the honesty of the user who submitted a particular report, our system offers a more secure reward-claiming model which protects against fraud while still preserving the user’s anonymity.

Suggested Citation

  • Stylianos S. Mamais & George Theodorakopoulos, 2017. "Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems," Future Internet, MDPI, vol. 9(4), pages 1-23, November.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:4:p:88-:d:119638
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    References listed on IDEAS

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    1. Sokratis Vidros & Constantinos Kolias & Georgios Kambourakis & Leman Akoglu, 2017. "Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset," Future Internet, MDPI, vol. 9(1), pages 1-19, March.
    2. Stylianos S. Mamais & George Theodorakopoulos, 2017. "Private and Secure Distribution of Targeted Advertisements to Mobile Phones," Future Internet, MDPI, vol. 9(2), pages 1-21, May.
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    Cited by:

    1. Georgios Kambourakis & Felix Gomez Marmol & Guojun Wang, 2018. "Security and Privacy in Wireless and Mobile Networks," Future Internet, MDPI, vol. 10(2), pages 1-3, February.

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