Title: Learning Maliciousness in Cybersecurity Graphs

Issue Number: Vol. 6, No. 3
Year of Publication: Sep - 2017
Page Numbers: 121-125
Authors: Liz Maida, Connor Walsh, Akshay Rangamani, Sam Gottlieb
Journal Name: International Journal of Cyber-Security and Digital Forensics (IJCSDF)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002277


Statistical relational learning is concerned with inferring patterns from data explicitly modeled as graphs. In this work, we present an approach to learning latent topological and attribute features of multi-relational property graphs in settings where a fraction of node attributes are missing. This work draws upon prior work based on tensor factorization. We demonstrate how learned latent embeddings can be used to approximate the missing attributes. The methods explored are applied to the problem of detecting malicious entities in a novel cybersecurity ontology in which emails are explicitly modeled as graphs