MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware
Abstract
Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomings in the existing ML approaches are likely contributing to this problem. The majority of current ML approaches ignore real-world challenges such as the detection of novel malware. In addition, proposed ML approaches are often designed either for malware/benign-ware classification or malware family classification. Here we introduce and showcase preliminary capabilities of a new method that can perform precise identification of novel malware families, while also unifying the capability for malware/benign-ware classification and malware family classification into a single framework.
Keywords:
non-negative matrix factorization, malware, semi-supervised learning, reject-option
Citation:
M. E. Eren, M. Bhattarai, K. Rasmussen, B. S. Alexandrov, and C. Nicholas, “MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware,” 2023 IEEE International Conference on Intelligence and Security Informatics (ISI), 2023, pp. 1-3,
BibTeX:
@INPROCEEDINGS{10297217,
author={Eren, Maksim E. and Bhattarai, Manish and Rasmussen, Kim and Alexandrov, Boian S. and Nicholas, Charles},
booktitle={2023 IEEE International Conference on Intelligence and Security Informatics (ISI)},
title={MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware},
year={2023},
volume={},
number={},
pages={1-3},
keywords={Cyberspace;Machine learning;Malware;Security;Task analysis;National security;Informatics;non-negative matrix factorization;malware;semi-supervised learning;reject-option},
doi={10.1109/ISI58743.2023.10297217}}
Maksim E. Eren
Scientist
My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.