NetDER: An Architecture for Reasoning About Malicious Behavior

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.