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Workshops of the International Conference on Advanced Information Networking and Applications

WAINA 2020: Web, Artificial Intelligence and Network Applications pp 429–439Cite as

Shilling Attacks Detection in Collaborative Recommender System: Challenges and Promise

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1150))

Abstract

The reliability of the recommender system is highly essential for the continuity of any system. Fake and malicious users may be spoiling system predictions reliability by inserting and injecting fake profiles called “shilling attacks” into the target recommender system. Thus, the detection of these attacks is necessary for any recommender system. Therefore, several shilling attacks detection approaches have proposed. In this work, we propose a survey for the recent detection methods, which pick up famous shilling attack models against the collaborative filtering recommender systems.

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Author information

Authors and Affiliations

  1. Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt

    Reda A. Zayed, Lamiaa F. Ibrahim & Hesham A. Hefny

  2. Management Information System Department, Higher Institute of Computer and Information Technology, Al Shorouk, Egypt

    Hesham A. Salman

Authors
  1. Reda A. Zayed
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  2. Lamiaa F. Ibrahim
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  3. Hesham A. Hefny
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  4. Hesham A. Salman
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Corresponding author

Correspondence to Lamiaa F. Ibrahim .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania Luigi Vanvitelli, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Zayed, R.A., Ibrahim, L.F., Hefny, H.A., Salman, H.A. (2020). Shilling Attacks Detection in Collaborative Recommender System: Challenges and Promise. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_39

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