Multi-feature Dataset for Windows PE Malware Classification

Authors: Muhammad Irfan Yousuf, Izza Anwer, Tanzeela Shakir, Minahil Siddiqui, Maysoon Shahid
Published: 2022-10-28

Source: https://arxiv.org/abs/2210.16285

PDF: https://arxiv.org/pdf/2210.16285

AIにより推定されたラベル

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

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

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.