Catch'em all: Classification of Rare, Prominent, and Novel Malware Families

Files

2403.02546.pdf (1.58 MB)

Links to Files

http://arxiv.org/abs/2403.02546

Permanent Link

https://doi.org/10.48550/arXiv.2403.02546
 http://hdl.handle.net/11603/32675

Collections

UMBC Faculty Collection
UMBC Computer Science and Electrical Engineering Department

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-9494-7139

Date

2024-03-04

Type of Work

journal articles
preprints
Text

Department

Program

Citation of Original Publication

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

Subjects

Computer Science - Cryptography and Security

Abstract

National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML) methods for analysis. However, shortcomings in existing ML approaches hinder their mass adoption. These challenges include detection of novel malware and the ability to perform malware classification in the face of class imbalance: a situation where malware families are not equally represented in the data. Our work addresses these shortcomings with MalwareDNA: an advanced dimensionality reduction and feature extraction framework. We demonstrate stable task performance under class imbalance for the following tasks: malware family classification and novel malware detection with a trade-off in increased abstention or reject-option rate.