Random Forest of Tensors (RFoT)

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.

Publication
Presented at the 12th Annual Malware Technical Exchange Meeting, Online, 2021

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
Maksim E. Eren
Computer Science Student

My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.