Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

Ran Liu, Maksim E. Eren, Charles Nicholas
July 2022
Poster Details

Abstract

With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are supervised solutions. These supervised classifiers often do not generalize well to novel malware. Therefore, they need to be re-trained frequently to detect new malware specimens, which can be time-consuming. Our work addresses this problem in a hybrid framework of theoretical Quantum ML, combined with feature selection strategies to reduce the data size and malware classifier training time.

Type
Preprint
Publication
Presented at the 13th Annual Malware Technical Exchange Meeting, Online, 2022

Keywords:

Tensors, Machine Learning, AV, Malware

Citation:

Liu, R., Eren, M. E., and Nicholas, C. (2022). Can Feature Engineering Help Quantum Machine Learning for Malware Detection?. Presented at the 13th Annual Malware Technical Exchange Meeting, Online, 2022.

BibTeX:

@misc{Liu2022MTEM,
      title={Can Feature Engineering Help Quantum Machine Learning for Malware Detection?}, 
      author={R. {Liu} and M. E. {Eren} and C. {Nicholas}},
      year={2022},
      note={Presented at the 13th Annual Malware Technical Exchange Meeting, Online, 2022}
}
CP Quantum Machine Learning Malware Cybersecurity
Maksim E. Eren
Maksim E. Eren
Scientist

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