ObservableDB: An Inverted Index for Graph-Based Traversal of Cyber Threat Intelligence


TOVARŇÁK Daniel ČECH Michal TICHÝ Dušan DOHNAL Vojtěch

Year of publication 2022
Type Article in Proceedings
Conference Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022
MU Faculty or unit

Institute of Computer Science

Web https://doi.org/10.1109/NOMS54207.2022.9789882
Doi http://dx.doi.org/10.1109/NOMS54207.2022.9789882
Keywords cyber threat intelligence; security; GraphQL
Description In this paper, we address the lack of analytical tools and search interfaces, which would help both humans and machines to navigate and correlate the floods of heterogeneous cyber threat intelligence (CTI) data generated every day. This work supports our long-term goal of machine-assisted discovery and inference of detectable indicators for adversarial tactics, techniques, and procedures from the available CTI. In particular, we present the idea of an observable database that works as an inverted index for CTI. This observable-centric concept is supported by a fully-functional practical result that leverages a meta-programming approach to auto-generate a graph-based API for data search and manipulation. The created prototype allows for powerful graph-based filtering, traversal and retrieval of the stored cyber observables and the referenced CTI.
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