Abstract
Machine learning has become an invaluable tool in the fight against malware. Traditional supervised and unsupervised methods are not designed to capture the multi-dimensional details that are often present in cyber data. In contrast, tensor factorization is a powerful unsupervised data analysis method for extracting the latent patterns that are hidden in a multi-dimensional corpus. In this poster we explore the application of tensors to classification, and we describe a hybrid model that leverages the strength of multi-dimensional analysis combined with clustering. We introduce a novel semi-supervised ensemble classifier named Random Forest of Tensors (RFoT) that is based on generating a forest of tensors in parallel, which share the same first dimension, and randomly selecting the remainder of the dimensions and entries of each tensor from the features set.
Keywords:
Tensors, Machine Learning, Ensemble, Semi-supervised, Malware
Citation:
Eren, M. E., Nicholas, C., McDonald, R., and Hamer, R. (2021). Random Forest of Tensors (RFoT). Presented at the 12th Annual Malware Technical Exchange Meeting, Online, 2021.
BibTeX:
@misc{eren2021RFoT,
title={Random Forest of Tensors (RFoT)},
author={M. E. {Eren} and C. {Nicholas} and R. {McDonald} and C. {Hamer}},
year={2021},
note={Presented at the 12th Annual Malware Technical Exchange Meeting, Online, 2021}
}
Maksim E. Eren
Scientist
My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.