Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity

LEIVA, MARIO A.; GARCÍA, ALEJANDRO JAVIER; SHAKARIAN, PAULO; SIMARI, GERARDO I.

Big Data and Cognitive Computing

MDPI

Año: 2022 vol. 6

Decision support tools are key components of intelligent sociotechnical systems, and theirsuccessful implementation faces a variety of challenges, including the multiplicity of informationsources, heterogeneous format, and constant changes. Handling such challenges requires the abilityto analyze and process inconsistent and incomplete information with varying degrees of associateduncertainty. Moreover, some domains require the system’s outputs to be explainable and interpretable;an example of this is cyberthreat analysis (CTA) in cybersecurity domains. In this paper, we firstpresent the P-DAQAP system, an extension of a recently developed query-answering platform basedon defeasible logic programming (DeLP) that incorporates a probabilistic model and focuses ondelivering these capabilities. After discussing the details of its design and implementation, anddescribing how it can be applied in a CTA use case, we report on the results of an empirical evaluationdesigned to explore the effectiveness and efficiency of a possible world sampling-based approximatequery answering approach that addresses the intractability of exact computations.