Labels Predicted by AI
悪意のある行為者の分類 ソフトウェアセキュリティ データラベリングの課題
Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.
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.