Title

Multifamily malware models

Publication Date

3-1-2020

Document Type

Article

Department

Computer Science

Publication Title

Journal of Computer Virology and Hacking Techniques

Volume

16

Issue

1

DOI

10.1007/s11416-019-00345-8

First Page

79

Last Page

92

Abstract

When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger results as compared to a case where we train a single model on multiple diverse families. However, during the detection phase, it would be more efficient to have a single model that can reliably detect multiple families, rather than having to score each sample against multiple models. In this research, we conduct experiments based on byte n-gram features to quantify the relationship between the generality of the training dataset and the accuracy of the corresponding machine learning models, all within the context of the malware detection problem. We find that neighborhood-based algorithms generalize surprisingly well, far outperforming the other machine learning techniques considered.

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