Automated Reasoning over Provenance-Aware Communication Network Knowledge in Support of Cyber-Situational Awareness

Cyber-situational awareness is crucial to applications such as network monitoring and management, vulnerability assessment, and defense. To gain improved cyber-situational awareness, analysts can benefit from automated reasoning-based frameworks. However, such frameworks would require the processing of enormous amounts of network data, which are characterized by syntactic variability. The formal representation of networking concepts, their properties, and interrelations using RDF can narrow the interoperability gaps between routing information and network semantics. Formal knowledge representation also enables automated reasoning, which facilitates network knowledge discovery by making implicit statements explicit. However, capturing and reasoning over the provenance of RDF statements, which is essential to build analysts’ trust in automated support tools, is not trivial. This paper presents a novel framework for capturing provenance-aware network knowledge to enable automated reasoning for network applications that require cyber-situational awareness.