  A major challenge in cyber-threat analysis is combining information from
different sources to find the person or the group responsible for the
cyber-attack. It is one of the most important technical and policy challenges
in cyber-security. The lack of ground truth for an individual responsible for
an attack has limited previous studies. In this paper, we take a first step
towards overcoming this limitation by building a dataset from the
capture-the-flag event held at DEFCON, and propose an argumentation model based
on a formal reasoning framework called DeLP (Defeasible Logic Programming)
designed to aid an analyst in attributing a cyber-attack. We build models from
latent variables to reduce the search space of culprits (attackers), and show
that this reduction significantly improves the performance of
classification-based approaches from 37% to 62% in identifying the attacker.

Artificial Intelligence