TITLE: Multi-feature Dataset for Windows PE Malware Classification 

ABSTRACT: This paper describes a multi-feature dataset for training machine learning
classifiers for detecting malicious Windows Portable Executable (PE) files. The
dataset includes four feature sets from 18,551 binary samples belonging to five
malware families including Spyware, Ransomware, Downloader, Backdoor and
Generic Malware. The feature sets include the list of DLLs and their functions,
values of different fields of PE Header and Sections. First, we explain the
data collection and creation phase and then we explain how did we label the
samples in it using VirusTotal's services. Finally, we explore the dataset to
describe how this dataset can benefit the researchers for static malware
analysis. The dataset is made public in the hope that it will help inspire
machine learning research for malware detection.
This paper describes a multi-feature dataset for training machine learning
classifiers for detecting malicious Windows Portable Executable (PE) files. The
dataset includes four feature sets from 18,551 binary samples belonging to five
malware families including Spyware, Ransomware, Downloader, Backdoor and
Generic Malware. The feature sets include the list of DLLs and their functions,
values of different fields of PE Header and Sections. First, we explain the
data collection and creation phase and then we explain how did we label the
samples in it using VirusTotal's services. Finally, we explore the dataset to
describe how this dataset can benefit the researchers for static malware
analysis. The dataset is made public in the hope that it will help inspire
machine learning research for malware detection.
Multi-feature Dataset for Windows PE Malware Classification
http://arxiv.org/abs/2210.16285v1