Deep Embedded Knowledge Graph Representations for Tactic Discovery

Using unsupervised Machine Learning (ML) techniques for the discovery of commonalities within a dataset is a well-established approach. However, existing clustering methods require relationships to be represented within the data, making discovery difficult a priori since unlabeled classes are discovered through exploratory clustering. To circumvent exploratory class labeling, we propose a feature-rich, connected structure (i.e., semantic graph), rather than a feature-engineered vectorization, enabling the encoding of maximal information despite class uncertainty. Expanding upon previous tactics discovery work, the authors present a systematic approach using knowledge graph representations and graph embeddings to discover tactics ab initio from data characteristics (DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. (OPSEC #4189)).