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NetDER: An Architecture for Reasoning About Malicious Behavior

Author

Listed:
  • Jose N. Paredes

    (UNS–CONICET)

  • Gerardo I. Simari

    (UNS–CONICET
    Arizona State University Tempe)

  • Maria Vanina Martinez

    (Universidad de Buenos Aires (UBA), and Institute for Computer Science Research, UBA–CONICET)

  • Marcelo A. Falappa

    (UNS–CONICET)

Abstract

Malicious behavior in social media has many faces, which for instance appear in the form of bots, sock puppets, creation and dissemination of fake news, Sybil attacks, and actors hiding behind multiple identities. In this paper, we propose the NetDER architecture, which takes its name from its two main modules: Net work D iffusion and ontological reasoning based on E xistential R ules), to address these issues. This initial proposal is meant to serve as a roadmap for research and development of tools to attack malicious behavior in social media, guiding the implementation of software in this domain, instead of a specific solution. Our working hypothesis is that these problems – and many others – can be effectively tackled by (i) combining multiple data sources that are constantly being updated, (ii) maintaining a knowledge base using logic-based formalisms capable of value invention to support generating hypotheses based on available data, and (iii) maintaining a related knowledge base with information regarding how actors are connected, and how information flows across their network. We show how these three basic tenets give rise to a general model that has the further capability of addressing multiple problems at once.

Suggested Citation

  • Jose N. Paredes & Gerardo I. Simari & Maria Vanina Martinez & Marcelo A. Falappa, 2021. "NetDER: An Architecture for Reasoning About Malicious Behavior," Information Systems Frontiers, Springer, vol. 23(1), pages 185-201, February.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:1:d:10.1007_s10796-020-10003-w
    DOI: 10.1007/s10796-020-10003-w
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    References listed on IDEAS

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    1. Matthew C Benigni & Kenneth Joseph & Kathleen M Carley, 2017. "Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-23, December.
    2. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
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    Cited by:

    1. Prabhsimran Singh & Surleen Kaur & Abdullah M. Baabdullah & Yogesh K. Dwivedi & Sandeep Sharma & Ravinder Singh Sawhney & Ronnie Das, 2023. "Is #SDG13 Trending Online? Insights from Climate Change Discussions on Twitter," Information Systems Frontiers, Springer, vol. 25(1), pages 199-219, February.
    2. Lisa Soetekouw & Spyros Angelopoulos, 2024. "Digital Resilience Through Training Protocols: Learning To Identify Fake News On Social Media," Information Systems Frontiers, Springer, vol. 26(2), pages 459-475, April.

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