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Exploring Non-Human Traffic in Online Digital Advertisements: Analysis and Prediction

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Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11684))

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Abstract

An advertisement (ad) click fraud occurs when a user or a bot clicks on an ad with a malicious intent where advertisers need to pay for those fake clicks. Click-fraud is a serious problem for the online advertising industry. Our study demonstrates a hybrid approach using a two-level fingerprint to detect the illegitimate bots targeting ad click fraud. The approach consists of two detection phases: (1) a rule-based phase and (2) a machine learning-based phase. The first level of the fingerprint is used for rule-based detection phase. It is generated using immutable information about the user and traversing a website’s page. The second level of the fingerprint is generated using ad click behavioral patterns. It is used for machine learning-based detection phase. Different traditional classification algorithms were evaluated to be applied in the machine learning-based detection phase. To test our approach, we used a real commercial website for ads called Waseet where the access log of the website server was utilized as a dataset for our experiments. The results of our experiments show that our proposed hybrid approach entails promising results.

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Author information

Authors and Affiliations

  1. Department of Computer Science, King Abdullah II School for Information Technology, The University of Jordan, Amman, 11942, Jordan

    Sawsan Almahmoud

  2. Department of Computer Information Systems, King Abdullah II School for Information Technology, The University of Jordan, Amman, 11942, Jordan

    Bassam Hammo

  3. Department of Information Technology, King Abdullah II School for Information Technology, The University of Jordan, Amman, 11942, Jordan

    Bashar Al-Shboul

Authors
  1. Sawsan Almahmoud
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  2. Bassam Hammo
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  3. Bashar Al-Shboul
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Corresponding author

Correspondence to Bashar Al-Shboul .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l'Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l'Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l'Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Almahmoud, S., Hammo, B., Al-Shboul, B. (2019). Exploring Non-Human Traffic in Online Digital Advertisements: Analysis and Prediction. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_57

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  • DOI: https://doi.org/10.1007/978-3-030-28374-2_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28373-5

  • Online ISBN: 978-3-030-28374-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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