Argumentation-based cyber attribution: The DeLP3E model

In cyber attribution, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. In this chapter, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is especially suitable for handling such contradictory and uncertain data–hence the framework would be well-suited for cyber attribution. We conclude with the demonstration—via a case study—of how our framework can be used to address the attribution problem in cybersecurity.