ObservableDB: An Inverted Index for Graph-Based Traversal of Cyber Threat Intelligence
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022 |
| MU Faculty or unit | |
| Citation | |
| 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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