Using Simulated Narratives to Understand Attribution in the Information Dimension

Conducting a measured response to cyber or information attack is predicated on attribution. When these operations are conducted covertly or through proxies, uncertainty in attribution limits response options. To increase attribution certainty in the information dimension, the authors have developed a suite of supervised machine learning models that attribute an emerging narrative to historical narratives from known actors. These models were first developed on simulated narratives produced with a Large Language Model. Once the supervised classification models were developed and tested on the simulated narratives, they are evaluated on known actor social media narratives from three known actors. The attribution models are language agnostic and offer one-vs-rest and multi-class options. All models performed at relatively high accuracy and can provide decision support for cyber response decisions.